CN113949955B

CN113949955B - Noise reduction processing method and device, electronic equipment, earphone and storage medium

Info

Publication number: CN113949955B
Application number: CN202010688695.2A
Authority: CN
Inventors: 张驰; 杨鹤飞
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2020-07-16
Filing date: 2020-07-16
Publication date: 2024-04-09
Anticipated expiration: 2040-07-16
Also published as: CN113949955A

Abstract

The application discloses a noise reduction processing method, a device, electronic equipment, an earphone and a storage medium, and relates to the technical field of noise reduction, wherein the method comprises the following steps: acquiring environmental sounds acquired by an audio acquisition device, wherein the environmental sounds comprise noise signals; preprocessing the environmental sound to obtain a noise signal to be analyzed, wherein the noise signal to be analyzed corresponds to a plurality of frequency bands; acquiring sound energy values of the noise signals to be analyzed in a plurality of frequency bands; determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the frequency bands; and carrying out noise reduction processing on the environmental sound based on the target noise reduction parameter. According to the method and the device, the target noise reduction parameters are determined according to the proportional relation among the sound energy values of the noise signals in the plurality of frequency bands through identification, so that targeted noise reduction processing is performed based on the target noise reduction parameters, and therefore a better active noise reduction effect can be achieved for the noise signals in various frequency bands encountered daily.

Description

Noise reduction processing method and device, electronic equipment, earphone and storage medium

Technical Field

The present disclosure relates to the field of noise reduction technologies, and in particular, to a noise reduction processing method, a device, an electronic device, an earphone, and a storage medium.

Background

At present, a method of fixing filter parameters is generally adopted for active noise reduction, but because of different noise compositions in different environments, surrounding noise is eliminated by using the fixed filter parameters, and when the noise in the surrounding environment is obviously changed, the noise reduction effect of active noise reduction is not stable enough. For example, the active noise reduction earphone with the noise reduction performance peak value between 80 and 200Hz can obviously reduce the active noise reduction performance between 400 and 2000Hz, and cannot effectively eliminate partial noise between 400 and 2000Hz in daily environmental noise, namely the noise reduction effect of the current active noise reduction earphone is poor.

Disclosure of Invention

The embodiment of the application provides a noise reduction processing method, a device, electronic equipment, an earphone and a storage medium, which can improve the active noise reduction effect.

In a first aspect, an embodiment of the present application provides a noise reduction processing method, where the method includes: acquiring environmental sounds acquired by an audio acquisition device, wherein the environmental sounds comprise noise signals; preprocessing the environmental sound to obtain a noise signal to be analyzed, wherein the noise signal to be analyzed corresponds to a plurality of frequency bands; acquiring sound energy values of the noise signals to be analyzed in a plurality of frequency bands; determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the frequency bands; and carrying out noise reduction processing on the environmental sound based on the target noise reduction parameter.

In a second aspect, an embodiment of the present application provides a noise reduction processing apparatus, including: the audio acquisition module is used for acquiring the environmental sound acquired by the audio acquisition device, wherein the environmental sound comprises a noise signal; the preprocessing module is used for preprocessing the environmental sound to obtain a noise signal to be analyzed, and the noise signal to be analyzed corresponds to a plurality of frequency bands; the energy acquisition module is used for acquiring sound energy values of the noise signal to be analyzed in a plurality of frequency bands; the parameter determining module is used for determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the frequency bands; and the noise reduction processing module is used for carrying out noise reduction processing on the environmental sound based on the target noise reduction parameters.

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory; one or more processors coupled with the memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the noise reduction processing method provided in the first aspect. In a fourth aspect, embodiments of the present application provide an earphone, including an audio acquisition device, an audio output device, and an audio signal processing circuit, where: the audio acquisition device is used for acquiring environmental sounds; the audio signal processing circuit is used for acquiring the environmental sound acquired by the audio acquisition device; preprocessing the environmental sound to obtain a noise signal to be analyzed, wherein the noise signal to be analyzed corresponds to a plurality of frequency bands; acquiring sound energy values of the noise signals to be analyzed in a plurality of frequency bands; determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the frequency bands; the audio output device is used for carrying out noise reduction processing on the environmental sound based on the target noise reduction parameter.

In a fifth aspect, embodiments of the present application provide a computer readable storage medium having program code stored therein, the program code being callable by a processor to perform the noise reduction processing method provided in the first aspect.

According to the noise reduction processing method, the device, the electronic equipment, the earphone and the storage medium, the environmental sound collected by the audio collection device is obtained, the environmental sound contains noise signals, then the environmental sound is preprocessed to obtain noise signals to be analyzed corresponding to a plurality of frequency bands, then sound energy values of the noise signals to be analyzed in the frequency bands are obtained, corresponding target noise reduction parameters are determined according to the proportional relation among the sound energy values of the frequency bands, and finally noise reduction processing is carried out on the environmental sound based on the noise reduction parameters. Therefore, the target noise reduction parameters are determined according to the proportional relation among the sound energy values of the noise signals in the plurality of frequency bands through identification, and the targeted noise reduction processing is carried out based on the target noise reduction parameters, so that the noise signals in various frequency bands encountered daily can be obtained, and a better active noise reduction effect can be achieved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 shows an active noise reduction schematic.

Fig. 2 shows a schematic view of an application environment suitable for use in embodiments of the present application.

Fig. 3 is a schematic flow chart of a noise reduction processing method according to an embodiment of the present application.

Fig. 4 is a schematic flow chart of a noise reduction processing method according to another embodiment of the present application.

Fig. 5 shows a schematic flow chart of step S260 in fig. 4 according to an exemplary embodiment of the present application.

Fig. 6 shows a spectrum characteristic diagram of a type of noise signal provided in an exemplary embodiment of the present application.

Fig. 7 illustrates a spectral signature of another type of noise signal provided by an exemplary embodiment of the present application.

Fig. 8 is a schematic flow chart of a noise reduction processing method according to another embodiment of the present application.

Fig. 9 shows a schematic flow chart of step S370 in fig. 8 according to an exemplary embodiment of the present application.

Fig. 10 shows a block diagram of a noise reduction processing apparatus provided in an embodiment of the present application.

Fig. 11 shows a block diagram of an electronic device according to an embodiment of the present application.

Fig. 12 shows a block diagram of a headset according to an embodiment of the present application.

Fig. 13 illustrates a storage unit for storing or carrying program codes for implementing the noise reduction processing method according to the embodiment of the present application provided in the embodiment of the present application.

Detailed Description

In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.

At present, active noise reduction (Active Noise Cancellation, ANC) headphones are mostly realized based on a fixed active noise reduction curve (Active Noise Cancellation Level Curve) by designing fixed active noise reduction parameters to eliminate external environment noise, the technical principle is as shown in fig. 1, after a microphone collects the external environment noise, the external environment noise is processed through a noise reduction circuit by using fixed noise reduction filter parameters, and an inverse signal is generated and broadcast through a loudspeaker to offset the external environment noise. After the fixed noise reduction filter parameters are adopted, the noise reduction performance curve does not change along with the external noise environment in the working process of the noise reduction circuit.

Definition of terms

Active noise reduction (Active Noise Cancellation, ANC): is a noise reduction method in which sound is played by a speaker based on a noise sound wave picked up at a specified position, and a sound wave having the opposite phase and the same amplitude as the original sound wave is generated at the specified position. Since acoustic waves are a type of mechanical vibration, linear superposition occurs when two acoustic waves meet in space. The secondary sound field and the original noise field generated by the playing of the loudspeaker meet at the appointed position and are in linear superposition, and the two sound waves with the same amplitude and opposite phases are mutually counteracted after being superposed, so that the noise is weakened or even eliminated, and a listener can obtain a quieter listening feeling.

Active noise reduction curve: the active noise reduction curve is a curve of the active noise reduction quantity of the noise reduction device along with the change of frequency, and is used for reflecting the noise reduction capability of the device at different frequencies of sound. The method is concretely characterized in that the vertical axis is a curve of active noise reduction amount and the horizontal axis is frequency. The amount of noise reduction of the noise reduction means represents the degree to which the audible sound wave is reduced before reaching the eardrum of the person. The noise reduction amounts of the active noise reduction on the sound waves with different frequencies are different, and compared with the high-frequency signals, the noise reduction effect of the active noise reduction on the low-frequency sound waves is more obvious. The noise reduction amount of the noise reduction device at each frequency point is measured by a standardized professional instrument, and a curve formed by connecting the noise reduction amounts of the frequency points is called a noise reduction curve, so that the noise reduction capacity of different frequency points is accurately described.

At present, the ANC earphone basically eliminates surrounding noise by using a fixed active noise reduction performance and a fixed active noise reduction curve, the effective range of active noise reduction of the earphone is generally 20-2000 Hz, and the peak value is generally 80-250 Hz.

But with a fixed active noise reduction curve to eliminate ambient noise, the noise reduction experience of ANC headphones is not stable when the ambient noise varies significantly. For example, an ANC earphone with a noise reduction performance peak value of 80-250 Hz has better active noise reduction experience when surrounding noise is mainly low-frequency noise of 80-250 Hz, but the active noise reduction experience is remarkably deteriorated when a user wears the earphone to enter an environment with surrounding noise mainly medium-low-frequency noise of 250-400 Hz. Eventually, the active noise reduction experience in different environments is significantly different when the user uses a general ANC headset.

In addition, a small part of ANC headphones can weaken noise of a part of frequency bands according to surrounding environment characteristics. For example, when such ANC headphones are used in conjunction with their handset end APP, the pass-through mode (i.e., amplifying ambient noise at the ear, enabling the wearer of the headphones to hear ambient noise more clearly, which function is similar to processing in the opposite direction of noise reduction) may be set to a "voice-related" mode. In a mode with respect to speech, the ear has a fixed amount of noise reduction for noise signals below about 300Hz while amplifying signals above about 300Hz (the frequency content of the speech signal is mainly above 300 Hz) to the ear of the wearer of the ear-piece. However, this process still uses a fixed active noise reduction curve to treat all different ambient noise equally, and the above-mentioned problems still remain.

In addition, the partial ANC earphone switches the noise reduction modes of different noise reduction intensity gears according to the overall intensity of the environmental noise, but the switching is realized automatically or manually, so that different frequency components in the noise cannot be flexibly distinguished.

Based on the above problems, embodiments of the present application provide a noise reduction processing method, apparatus, electronic device, and computer readable storage medium, which process and analyze an acquired environmental noise signal to identify a spectral characteristic of environmental noise, and adjust a noise reduction parameter according to the spectral characteristic of the noise, so as to obtain an optimal noise reduction effect for the noise. In order to facilitate a better understanding of the embodiments of the present application, an application environment suitable for the embodiments of the present application is described below.

Referring to fig. 2, fig. 2 shows a schematic view of an application environment suitable for use in an embodiment of the present application. The noise reduction processing method provided in the embodiment of the present application may be applied to the noise reduction processing system 10 shown in fig. 2. The noise reduction processing system 10 includes a terminal 100 and a headset 200.

The terminal 100 may be, but is not limited to, a mobile phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer iii, dynamic image compression standard audio layer 3), an MP4 (Moving Picture Experts Group Audio Layer iv, dynamic image compression standard audio layer 4) player, a notebook computer, an electronic book, or a wearable electronic device. The embodiment of the present application does not limit the specific device type of the terminal 100.

In some embodiments, an application program capable of controlling a headset noise reduction mode may be installed in the terminal 100. The terminal 100 may transmit the noise reduction pattern to be implemented to the earphone 200, and the earphone 200 plays audio, which may include inverted sound waves, music signals, masking sounds, etc. The terminal 100 may include an audio collection device, which is configured to collect environmental sounds, and may be provided with a processor, which may be configured to execute the noise reduction processing method provided in the embodiment of the present application, and then play audio correspondingly through the earphone 200 to achieve noise reduction.

Optionally, if the terminal 100 and the earphone 200 are based on wireless connection, the terminal 100 and the earphone 200 may be wirelessly connected based on Bluetooth (Bluetooth), 2.4G wireless communication technology, infrared transmission technology or wireless network to realize data transmission, for example, after the earphone 200 is wirelessly connected with the terminal 100, the terminal 100 may acquire audio data for playing. Alternatively, the wireless network may be a mobile communication network or a wireless fidelity network (Wireless Fidelity, wiFi).

The earphone 200 may be a wired earphone or a wireless earphone. Alternatively, the headset 200 may also be embodied as a true wireless headset. In the following, a fully wireless cable-free real wireless earphone is described as an example, but it should be apparent to those skilled in the art that the fully wireless cable-free real wireless earphone is merely illustrative, and in practical use, those skilled in the art may refer to the solution of the embodiment of the present application, and select other types of earphone to implement the present embodiment, including but not limited to a wired earphone and a wireless earphone with a cable between two earphones.

In addition, in some embodiments, the noise reduction processing system 10 may also include only the earphone 200, that is, the noise reduction processing method provided in the embodiments of the present application may be implemented without a terminal, for example, in a scenario where the earphone 200 does not play music and only performs noise reduction processing, the earphone 200 may not be connected to the terminal 100, and the noise reduction processing method provided in the embodiments of the present application may be implemented separately.

In addition, only one earphone is shown in the drawings, and in practical application, a person skilled in the art may refer to the solution of the embodiment of the present application to select a pair of earphones to implement the embodiment, and it should be noted that noise reduction processing of a plurality of earphones may be independent or not independent, and each earphone of the pair of earphones may be connected to the terminal 100 respectively, or may be connected to each other between the earphones, which is not limited in the embodiment of the present application.

In some embodiments, each earphone 200 may include an audio acquisition device, an audio output device, and an audio signal processing circuit, in particular may include at least 1 speaker, at least 1 microphone that may pick up ambient noise, at least 1 audio signal processing circuit that may run an algorithm, and further, the earphone 200 may include at least 1 power supply circuit.

The speaker is used for playing audio and ANC inverse noise, so as to realize the functions of playing music and ANC noise reduction of the earphone 200. When the headphones 200 are in the form of a mono headset or a truly wireless headset, each headphone 200 has at least 1 speaker. When the headphones 200 are in the form of two-channel headphones or multi-channel headphones, each headphones 200 has at least 2 speakers.

Wherein the microphone is located at a position on the structure of the headset 200 where ambient sounds can be picked up for at least two purposes: the original noise signal required by ANC noise reduction is used as an input signal required by the noise reduction circuit to output inverse noise; the noise detection analyzes the desired ambient noise signal. The two purposes can be realized by 1 microphone at the same time, so that the hardware cost is saved, and the two purposes can be realized by a plurality of microphones respectively.

Wherein the audio signal processing circuit may be used for two purposes: an ANC noise reduction processing function, configured to determine noise reduction parameters for noise reduction processing and send the noise reduction parameters to a speaker to output corresponding inverted sound waves, so as to implement noise reduction processing; and a noise detection analysis function for detecting and analyzing a noise signal in the audio signal.

The power supply circuit may supply power to other hardware components, and the power supply source may be a battery built in the earphone 200, may be an external power input, or may be a power generation device built in the earphone 200.

The noise reduction processing method, the device, the electronic equipment, the earphone and the storage medium provided by the embodiment of the application are described in detail below through specific embodiments.

Referring to fig. 3, fig. 3 shows a flowchart of a noise reduction processing method provided in an embodiment of the present application, which may be applied to an electronic device, and the electronic device may be the terminal or the earphone. The flow diagram shown in fig. 3 will be described in detail. The noise reduction processing method may include the steps of:

step S110: and acquiring the environmental sound acquired by the audio acquisition device.

The environment sound is a sound signal of the environment collected by the audio collection device based on the current position, and the environment sound comprises a noise signal. The audio acquisition device can be arranged at the terminal or at the earphone, and the environmental sound is acquired based on the audio acquisition device. In some embodiments, the audio capturing device may be a microphone or the like that may be used to capture sound signals, without limitation.

Taking the example that the audio collection device is arranged on the earphone, the environmental sound can be collected by the audio collection device based on the earphone, such as a microphone. When the device is arranged on the earphone, the cost can be saved, and the external environment sound signals picked up by the feedforward microphone can be used for feedforward noise reduction design and picking up user input voice without generating extra hardware cost.

In some embodiments, the audio collection device may collect the environmental sound in real time, and in other embodiments, the audio collection device may collect the environmental sound based on the collection instruction, that is, the electronic device may collect the environmental sound based on the audio collection device according to the collection instruction when receiving the collection instruction. For example, the user may trigger the acquisition instruction through the terminal, or through operation of the headset, so that the audio acquisition device may receive the acquisition instruction to acquire the ambient sound.

In some embodiments, the electronic device acquires the environmental sound acquired by the audio acquisition device, and if the environmental sound includes a noise signal, the active noise reduction function is started to execute step S120 and the following steps.

In other embodiments, the electronic device may also start the active noise reduction function after the environmental sound collected by the audio collecting device is obtained, if the sound energy value of the noise signal in the environmental sound exceeds the preset energy value, execute the step S120 and the subsequent steps to perform the noise reduction process on the noise, and if the sound energy value of the noise signal in the environmental sound does not exceed the preset energy value, may not execute the step S120 and the subsequent steps, for example, may continue to monitor the environmental sound or end the monitoring, so that the active noise reduction process may not be performed when the noise signal in the environmental sound is weaker and the influence on the user is not great, thereby reducing the power consumption of the device and saving resources.

Step S120: and preprocessing the collected environmental sound to obtain a noise signal to be analyzed.

In some embodiments, preprocessing the collected environmental sounds may include performing analog-to-digital conversion on the collected environmental sounds to obtain a digital signal, and performing pre-emphasis, framing, windowing, mel-frequency cepstral coefficient (Mel-Frequency Cepstral Coefficients, MFCC) extraction and the like on the digital signal to obtain a noise signal to be analyzed. In other embodiments, the pretreatment may include more or fewer processing steps than those previously described, without limitation.

In this embodiment, the collected environmental sound is preprocessed, and the noise signal to be analyzed may be divided into a plurality of frequency bands. Specifically, the frequency band refers to the frequency range of the signal, and the unit is generally hertz (Hz), for example, the frequency band may be 400Hz to 600Hz, and the noise signal to be analyzed may correspond to 100Hz to 200Hz, 200Hz to 400Hz, and 400Hz to 600Hz.

Step S130: sound energy values of a noise signal to be analyzed in a plurality of frequency bands are acquired.

After obtaining the noise signal to be analyzed, the sound energy values of the noise signal to be analyzed in a plurality of frequency bands can be obtained, and in one implementation mode, the noise signal corresponding to the plurality of frequency bands is obtained, and the sound energy values of the noise signal to be analyzed in the plurality of frequency bands can be obtained according to the sound energy values of the noise signal in each frequency band.

Where the sound energy value may be in dB, in some examples the spectral energy may also be referred to as energy, amplitude, sound pressure level, i.e. how much dB a sound signal is represented in the environment. As one way, according to the distribution of the noise signal to be analyzed on the spectrogram, the horizontal axis of the spectrogram may be frequency, the vertical axis may be sound energy value, and then the sound energy values of the noise signal in a plurality of frequency bands may be determined.

Step 140: and determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the plurality of frequency bands.

When the noise reduction processing is performed on the environmental sound, corresponding target noise reduction parameters can be determined according to the proportional relation among sound energy values of a plurality of frequency bands of the noise signal, so that the noise signal in the environmental sound is subjected to the noise reduction processing based on the target noise reduction parameters. The frequency band with more concentrated sound energy values can be more accurately determined by acquiring the proportional relation among the sound energy values of the plurality of frequency bands, so that the noise types can be more accurately distinguished, and more accurate noise reduction parameters can be determined, and better noise reduction effect can be achieved.

The target noise reduction parameters may be generated in real time according to the sound energy values of the collected noise signals, or may be preset, for example, in some embodiments, multiple sets of preset noise reduction parameters for various noise signals may be preset, and a mapping relationship between each preset noise reduction parameter and a proportional relationship may be built, so that according to the proportional relationship between the sound energy values of multiple frequency bands, the corresponding preset noise reduction parameters are determined as the target noise reduction parameters, so that noise reduction processing is performed on the environmental sound based on the target noise reduction parameters, so that the operand may be reduced, and if the embodiment is applied to headphones, the power consumption of the headphones may be reduced, and the endurance time of the headphones may be increased.

In some embodiments, the noise type of the noise signal to be analyzed may be determined based on the trained neural network model according to the proportional relation of the energy values of the multiple frequency bands, and then the noise reduction parameter corresponding to the noise type is determined as the target noise reduction parameter, so that the neural network model may be used as a training sample by the collected noise signals of the various frequency bands, and the noise types of the training samples are marked, so that training the neural network model to obtain the trained neural network model may be used to implement step S140.

In other embodiments, the noise reduction parameter corresponding to the frequency band with the highest sound energy value may be determined as the target noise reduction parameter by comparing the sound energy values of the plurality of frequency bands. In some embodiments, the proportional relationship between the sound energy values of the plurality of frequency bands may be obtained by determining the frequency band with the highest sound energy value and then determining the proportional relationship between the frequency band and the other frequency bands. The following embodiments are also specifically described, and will not be described in detail herein.

In other embodiments, the proportional relationship between the sound energy values of the plurality of frequency bands may also be directly determined by the ratio between the sound energy values of the plurality of frequency bands, and specifically, if the ratio between the sound energy values of the plurality of frequency bands matches the preset ratio, the frequency band with the highest sound energy value from the plurality of frequency bands may be determined as the candidate frequency band, and the noise reduction parameter corresponding to the candidate frequency band may be determined as the target noise reduction parameter. The preset ratio may be determined according to actual needs, for example, if the number of the plurality of frequency bands is 3, the preset ratio may be 1:1: 2. 1:1: 3. 1:2:4, etc., and is not limited herein. In addition, the matching with the preset ratio may be an exact matching or an approximate matching, for example, the ratio between the sound energy values of the three frequency bands is 1.1:1:2, the preset ratio is 1:1:2, at this time, it can also be determined that the ratio of the three frequency bands is matched with the preset ratio, so that a certain error can be allowed, and the error allowance degree can also be determined according to actual needs. In addition, the order of the frequency bands may not be limited when the ratio of the plurality of frequency bands is matched with the preset ratio, that is, as long as the ratio of the plurality of frequency bands can be matched with the preset ratio.

In a specific example, if the ratio between the sound energy values of the frequency band a, the frequency band B, and the frequency band C is 1:2:1, the preset proportion is 1:1:2, it may be determined that the ratio between the sound energy values of the frequency band a, the frequency band B, and the frequency band C matches the preset ratio, and the noise reduction parameter corresponding to the frequency band B may be determined as the target noise reduction parameter.

Step 150: and denoising the environmental sound based on the target denoising parameter.

The determined target noise reduction parameter may be an active noise reduction curve or a noise reduction parameter corresponding to the active noise reduction curve. The corresponding inverted sound wave may be output by the earphone to perform noise reduction processing on the ambient sound based on the target noise reduction parameter.

According to the noise reduction processing method, environmental sounds are acquired through the audio acquisition device, wherein the environmental sounds possibly comprise noise signals, then the acquired environmental sounds are preprocessed to obtain noise signals to be analyzed corresponding to a plurality of frequency bands, then sound energy values of the noise signals to be analyzed in the frequency bands are acquired, corresponding target noise reduction parameters are determined according to proportional relations among the sound energy values of the frequency bands, and finally noise reduction processing is performed on the environmental sounds based on the noise reduction parameters. Therefore, the target noise reduction parameters are determined according to the proportional relation among the sound energy values of the noise signals in the plurality of frequency bands through identification, and the targeted noise reduction processing is carried out based on the target noise reduction parameters, so that the noise signals in various frequency bands encountered daily can be obtained, and a better active noise reduction effect can be achieved.

Referring to fig. 4, fig. 4 is a schematic flow chart of a noise reduction processing method according to another embodiment of the present application, and specifically, the method may include:

step S210: environmental sounds are collected based on the audio collection device.

Step S220: based on the preset frequency range, dividing the preset frequency range into a plurality of frequency bands to be analyzed according to octaves.

Because the resolution of the human ear hearing system to the sound frequency is not fixed, the higher the frequency is, the lower the frequency resolution of the human ear hearing system is, and the frequency interval which can be resolved by the human ear hearing system is approximately in logarithmic proportion to the center frequency of the octave, when the noise signal to be analyzed corresponding to a plurality of frequency bands is obtained, the frequency bandwidth of the noise signal to be analyzed can be set to be logarithmic frequency bands, such as the octave. In some embodiments, if finer granularity of noise signal identification is desired, the analysis bandwidth may be set to 1/2 octave or even 1/3 octave, which is not limited in this embodiment.

In some embodiments, the preset frequency range may be determined according to practical needs, for example, considering that the noise reduction effect of most ANC headphones in the frequency band of 1-2 kHz is weak, and only the low-frequency noise component with the frequency below 1kHz may be analyzed, the preset frequency range may be the frequency range below 1 kHz. Of course, a wider or narrower frequency range may be processed and analyzed, and the preset frequency range is not limited in this embodiment.

In some embodiments, the division of the frequency bands may also be determined according to actual needs. In practical application, in order to avoid the ANC earphone entering the nonlinear working area in the strong low-frequency vibration occasion and generating abnormal sound, the noise reduction effect of the ANC earphone below 100Hz is generally poor. In view of this, the first frequency band to be analyzed may be set to 100 to 200Hz, while the noise signal may be subjected to logarithmic frequency range analysis in combination with the frequency resolution performance of the human ear auditory system, and the second frequency band to be analyzed may be set to 200 to 400Hz. In addition, as an embodiment, the third frequency band to be analyzed may be set to 400 to 800Hz according to the logarithmic octave characteristic.

As another embodiment, considering that the noise reduction effect of most ANC headphones is weak in the 1-2 kHz band, only the low-frequency noise component with the frequency below 1kHz may be analyzed, and only the frequency spectrum component below 1kHz may be considered, and the third band to be analyzed may be set to 400-1000 Hz. Thus, the frequency ranges of the different frequency bands to be analyzed can be determined in combination with the frequency characteristics of active noise reduction and the characteristics of the human ear auditory system.

It should be noted that, the noise reduction performance of different ANC headphones is different, and according to the noise reduction performance of the ANC headphones in different frequency bands, the third frequency band to be analyzed may be flexibly set, for example, the highest frequency at which the noise reduction performance begins to deteriorate is set as the upper limit frequency value of the third frequency band to be analyzed. In one example, if the noise reduction effect of the ANC earphone in the frequency band above 1.2kHz is not degraded or is lower than the set threshold, the third frequency band to be analyzed may be set to 400-1200 Hz, where the preset frequency range may be a frequency range below 1.2 kHz.

The frequency band to be analyzed may be set to be narrower or wider, the center frequency may be shifted to other frequencies, the frequency band may be split into a plurality of finer frequency bands, and the like, which is not limited in this embodiment. In addition, the log-frequency analysis may be based on octaves, 1/2 octaves, 1/3 octaves, or the like, which is not limited in this embodiment.

Consider that a human ear listening system has a logarithmic characteristic in its ability to resolve sound frequencies, i.e. high resolution for low frequency parts but rather low resolution for high frequency parts. Therefore, compared with the method that the noise signals picked up by the audio acquisition device are subjected to Fourier transformation, and then the noise spectrum difference is analyzed to call the noise reduction methods of different noise reduction modes, the method can be used for identifying the noise by combining the noise spectrum characteristics in the frequency band after the frequency band to be analyzed is set, so that the subjective listening characteristics of the human ear listening system can be fully considered, the consideration of excessive noise spectrum details is reduced, and the noise signals are better in robustness in processing and analyzing.

Step S230: and determining the corresponding band-pass filter according to the upper limit frequency value and the lower limit frequency value of each frequency band.

According to the upper limit frequency value and the lower limit frequency value of each frequency band, the frequency range of each frequency band can be obtained, and then the band-pass filter corresponding to each frequency range can be generated.

Step S240: and carrying out band-pass filtering processing on the noise signal of each frequency band in the time domain based on the band-pass filter corresponding to each frequency band, and obtaining the noise signal after filtering as the noise signal to be analyzed.

And carrying out band-pass filtering processing on the noise signals of each frequency band in the time domain based on the band-pass filter corresponding to each frequency band, and filtering out signals in different frequency bands to be analyzed so as to facilitate the subsequent identification of the spectral characteristics of the environmental noise signals. In the filtering process, unnecessary low frequency and high frequency are filtered, so that the integrity of data information is ensured, and the data volume of signal processing is reduced. And because the filtering is directly carried out on the time domain, fourier transformation is not needed to be carried out to the frequency spectrum, and the filtering is only carried out on the time domain, the processing is simpler, and the noise reduction characteristic is more met.

Wherein the signal processing can retain frequency components in a certain frequency range in the signal while attenuating frequency components outside the frequency range to a lower level by the band-pass filtering processing. In this embodiment, the band-pass filtering process may be used to intercept sound signals in different frequency ranges, and then calculate the sound signal energy in the corresponding frequency range, that is, the sound energy value, and further distinguish different noise types according to the distribution difference of the sound energy value in different frequency ranges, so as to determine the corresponding noise reduction parameter as the target noise reduction parameter.

In other embodiments, low-pass filtering may be performed instead of bandpass filtering, which is not limited herein.

In some embodiments, the preprocessing may further include a downsampling process, for example, before the aforementioned band-pass filtering process is performed on the noise signal of each frequency band in the time domain based on the band-pass filter corresponding to each frequency band, the downsampling process may be performed on the collected environmental sound to obtain a downsampled environmental sound; and obtaining a noise signal to be analyzed according to the down-sampled environmental sound. Specifically, in some embodiments, the downsampled ambient sound is obtained, and the downsampled ambient sound may be preprocessed to obtain the noise signal to be analyzed, where the preprocessing step may refer to the foregoing step S120, and is not described herein again. Of course, in other embodiments, when the down-sampled ambient sound is preprocessed, the noise signal of the ambient sound may also be filtered, and the specific embodiments refer to the foregoing steps S220 to S240, which are not described herein again.

Because active noise reduction processing places high demands on the latency of the system, for example, in some scenarios, the hardware latency of the system needs to be within 20 microseconds. Thus, in the active noise reduction processing path, the sampling rate of the digital signal is high, substantially above 192kHz, even with some ANC headphones also employing high sampling rates of 768 kHz. However, when the noise spectrum is subjected to the recognition analysis and the classification processing, the sensitivity to the system delay is much lower, and the higher the sampling rate is, the larger the calculation amount is considered, so that the digital signal with a high sampling rate can be subjected to the downsampling processing before the noise spectrum recognition analysis is performed.

In some embodiments, since active noise reduction is primarily effective for frequency components below 2kHz, noise signals in this frequency range may be identified for analysis. In order to cover the frequency range below 2kHz, it is sufficient that the microphone signal sampling rate is only not lower than 4 kHz. Meanwhile, the lower the frequency is, the more the operand can be saved, so that the downsampling process can be adopted; higher sampling rates may also be used if the computing power of the electronic device is sufficient, such as the computing power of the audio signal processing circuit. As one way, a sampling rate of 16kHz may be employed, and the acquired ambient sound may be downsampled based on the sampling rate of 16 kHz; as another mode, it is also conceivable to use different sampling rates, and only the sampling rate is required to be ensured to be not lower than 4kHz, so that the calculation amount can be reduced to a certain extent, and the accuracy of the noise signal can be ensured.

Step S250: sound energy values of a noise signal to be analyzed in a plurality of frequency bands are acquired.

The noise components of the noise signal in different frequency bands to be analyzed can be obtained through band-pass filtering processing, and at the moment, the sound energy values of the noise signal to be analyzed in different frequency bands can be calculated.

In some embodiments, after obtaining the sound energy values of the noise signal to be analyzed in the plurality of frequency bands, the sound energy values of the noise signal to be analyzed in the plurality of frequency bands may be smoothed. Because the noise signals always change with time in the environment, in actual use, in order to ensure that the active noise reduction effect is not required to be frequently switched and the use experience is poor, the sound energy values of the noise signals in different frequency band ranges are subjected to smoothing treatment, wherein the specific smoothing speed can be adjusted according to actual needs, can be preset by a program and can be customized by a user. The tracking speed of the noise signal change can be slowed down through the energy smoothing process, the influence of some transient changes of the noise signal of the environment on the noise reduction effect can be eliminated, and the user experience is improved.

Step S260: and determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the plurality of frequency bands.

In other embodiments, step S260 may be implemented based on a trained neural network model, and step S260 may include steps S261 to S262, specifically, referring to fig. 5, fig. 5 shows a schematic flow chart of step S260 in fig. 4 provided in an exemplary embodiment of the present application, where step S260 may include:

step S261: based on the trained deep learning model, the noise type is determined according to the proportional relation of the sound energy values of a plurality of frequency bands.

The noise database may be collected before training the deep learning model, for example, noise signals in different environments may be collected based on different noise environments, so as to obtain corresponding noise signals. In order to improve the recognition accuracy of the deep learning model, noise signals can be collected as much as possible.

After the noise signal is acquired, the acquired noise signal can be subjected to segmentation processing based on a preset time length to obtain a plurality of segments of noise signals with duration time length being the preset time length, wherein the preset time length can be determined according to actual needs, or can be preset by a program or user-defined, and the embodiment is not limited to the above. In one example, the preset time length may be set to be different from several seconds to several tens of seconds.

After the collected noise signals are segmented, the sound energy values of each segment of noise signals in different frequency bands can be marked. In some embodiments, only the sound energy value of the noise signal at each frequency band may be marked; in other embodiments, the proportional relationship of the sound energy values of the noise signal in multiple frequency bands may also be directly marked; in still other embodiments, the noise type of the noise signal may be further marked, which is not limited in this embodiment, and may be specifically determined according to the input-output definition when the deep learning model is constructed.

Wherein the noise types may be divided by various dimensions, and in some embodiments, by the frequency band with the highest sound energy value; in other embodiments, the noise generation scenarios may be partitioned by the difference; in still other embodiments, noise generation may be divided into, for example, snoring, air conditioning, speech, etc. The present embodiment does not limit the division manner of the noise types.

In one embodiment, since the sound energy value distributions of different noises on different frequency bands are different, the frequency bands with the highest sound energy values of different noises may also be different, and the noise types may be divided according to the frequency bands with the highest sound energy values, for example, the frequency band with the highest sound energy value of the noise type 1 is below 200Hz, the frequency band with the highest sound energy value of the noise type 2 is 500Hz to 600Hz, and if the sound energy values of the noise signals are mainly concentrated at 500Hz to 600Hz, the noise type may be marked as the noise type 2.

In addition, in some embodiments, when determining a frequency band with the highest sound energy value from the plurality of frequency bands, it may also be determined whether the ratio of the frequency band with the highest sound energy value to the sound energy values of other frequency bands exceeds a preset ratio, and if both the ratio exceeds the preset ratio, the frequency band with the highest sound energy value is marked as a frequency band corresponding to a noise type. If the two types of the frequency bands do not exceed each other, the frequency band with the highest sound energy value can be widened, and other frequency bands with the ratio not exceeding the preset ratio and the candidate frequency band are combined into a target frequency band, so that the combined target frequency band can be marked as a frequency band corresponding to a noise type, and corresponding noise reduction parameters are generated as the noise reduction parameters corresponding to the noise type aiming at the combined target frequency band, so that more accurate targeted noise reduction is realized. The specific embodiments thereof can be seen in the examples described below, and are not described herein.

In another embodiment, since the spectral characteristics of the noise in different frequency bands are different, the noise types may be classified according to the scenes, for example, the noise types may include subway noise in a subway environment, office noise in an office environment, and the like, then the subway noise may be collected based on the subway environment, the noise signals of various noise types such as office noise may be collected based on the office environment, and the corresponding noise types may be labeled, when the deep learning model is trained, the spectral characteristics of the noise signals may be analyzed, for example, the proportional relationship of the noise signals in a plurality of frequency bands may be used as the input of the deep learning model, the noise type corresponding to the noise signals may be used as the expected output, so that in one example, if the spectral characteristics of the collected noise signals are matched with the spectral characteristics of the subway noise collected based on the subway environment, for example, the noise type of the noise signals may be determined to be subway noise, wherein the determination of whether the spectral characteristics are matched may be performed by training the deep learning model, and the deep learning model is realized by training the deep learning model.

In yet another embodiment, since the spectral characteristics of the noise generated in different manners have large differences, for example, the sound energy value of the snore is mainly concentrated in 250-800 Hz, and the general noise reduction curve of the active noise reduction earphone can only have a better noise reduction effect on the noise signal at 80-200 Hz, so the noise reduction effect on the snore is not strong, in order to promote the targeted noise reduction effect, the noise types can be divided according to different noise generation manners, for example, the noise signals of various noise types such as the snore, the air conditioner, the speaking sound, and the like can be collected in advance, and the corresponding noise types can be marked, and when the deep learning model is trained, the foregoing description can be referred to, so in one example, if the proportion relation between the sound energy values of the collected noise signal in a plurality of frequency bands and the proportion relation between the sound energy values of the snore in the corresponding frequency bands are matched, the noise type of the noise signal can be determined as the snore.

And establishing a deep learning model based on the neural network, training the deep learning model based on the marked noise signals, training parameters of the deep learning model, including network layers, activation functions and the like, so as to obtain a desirable range of training parameters, judging whether training can be stopped according to a loss function curve obtained by training and testing, and obtaining the deep learning model capable of identifying different noise types when the training can be stopped. The neural network may be a convolutional neural network (Convolutional Neural Networks, CNN), a deep neural network (Deep Neural Networks, DNN), a recurrent neural network (Recurrent Neural Network, RNN), or the like, which is not limited herein. Thus, the noise type of the noise signal to be analyzed can be determined based on the trained deep learning model according to the proportional relation of the sound energy values of the plurality of frequency bands.

In some embodiments, the trained deep learning model may be transplanted into the audio signal processing circuitry of the headset, which may then be run based on the headset. In other embodiments, the training model can be deployed at the terminal, so that the training model can be operated based on the terminal, and therefore, the operation load of the earphone is reduced, and the power consumption of the earphone is reduced. Especially when the earphone is a true wireless earphone, the endurance time of the earphone can be improved.

Step S262: and determining the noise reduction parameter corresponding to the noise type as a target noise reduction parameter.

In some embodiments, a mapping relationship between each noise type and the noise reduction parameter may be stored in advance, so that the corresponding noise reduction parameter may be determined as the target noise reduction parameter according to the noise type. In other embodiments, the corresponding noise reduction parameter may be generated in real time according to the noise type as the target noise reduction parameter, so as to implement adaptive noise reduction processing for the current noise type, so as to obtain a better noise reduction effect.

In some embodiments, at least 3 sets of noise reduction parameters may be stored in the earphone, and the 3 sets of active noise reduction curves may be respectively corresponding to each other, and noise reduction processes of different noise types may be respectively matched.

Step S270: and denoising the environmental sound based on the target denoising parameter.

After the noise type is determined, corresponding target noise reduction parameters can be determined according to the noise reduction parameters corresponding to the noise type. For example, if the energy of the noise signal of the current environment is concentrated in the frequency band below 200Hz, the active noise reduction curve of the ANC earphone can be adjusted to an active noise reduction curve with the noise reduction performance concentrated in the frequency band below 200Hz, so as to obtain the optimal noise reduction effect; when the environment changes, the energy in the frequency spectrum of the noise signal is mainly concentrated at 400-600 Hz, and the active noise reduction curve of the ANC earphone can be adjusted to be concentrated at the frequency band of 400-600 Hz, so that the better noise reduction effect is continuously obtained.

In a specific example, taking fig. 6 and fig. 7 as examples, fig. 6 and fig. 7 are respectively spectrum feature diagrams of two types of noise signals. In fig. 6, the energy of the noise signal is concentrated in the bass frequency band below 200Hz, when the spectral characteristics of the noise signal are identified as shown in fig. 6, the noise reduction parameter with the noise reduction performance mainly concentrated below 200Hz may be determined as the target noise reduction parameter, and the noise reduction processing is performed on the environmental sound according to the target noise reduction parameter, for example, the active noise reduction curve is adjusted to have the noise reduction performance concentrated below 200 Hz. In fig. 7, the energy of the noise signal is more distributed in the vicinity of 500Hz to 600Hz, when the spectral characteristics of the noise signal are identified as shown in fig. 7, the noise reduction parameter with the noise reduction performance mainly concentrated between 500Hz and 600Hz can be determined as the target noise reduction parameter, and noise reduction processing is performed on the environmental sound by using the noise reduction parameter, for example, an active noise reduction curve is adjusted to have the noise reduction performance concentrated in the vicinity of 500Hz to 600Hz, and the earphone can perform noise reduction processing based on the active noise reduction curve.

It should be noted that, in this embodiment, the portions not described in detail may refer to the foregoing embodiments, and are not described herein again.

According to the noise reduction processing method, the environmental sound is collected based on the audio collection device, the corresponding band-pass filter is generated based on the multiple frequency bands to be analyzed which are divided by octaves, and the band-pass filter processing is directly carried out on the time domain to obtain the noise signal to be analyzed, so that the processing is simpler, and the noise reduction characteristics are more met. And then, according to the proportional relation between the sound energy values of the noise signals to be analyzed in a plurality of frequency bands, identifying and obtaining the frequency spectrum characteristics of the noise signals, and correspondingly adjusting noise reduction parameters through the frequency spectrum characteristics, thereby obtaining better noise reduction effects under different noises. In addition, the collected environmental sound can be subjected to downsampling before bandpass filtering, so that the calculation amount of the earphone can be considered, and the power consumption of the earphone can be reduced. The frequency bands to be analyzed are divided by adopting octaves which are more in line with the human ear hearing system, so that the noise type can be identified by the subjective perception characteristic which is more in line with the human ear hearing system, the subjective hearing and noise reduction effect which is more in line with the human ear can be achieved, and the subjective experience of a listener can be improved under the premise that excessive noise spectrum details are not required to be considered. In addition, due to the noise reduction processing method provided by the embodiment, a pair of ANC headphones can realize noise reduction processing on various noises in daily life, and the number of ANC headphones used and purchased by a user is saved.

Referring to fig. 8, fig. 8 is a flowchart illustrating a noise reduction processing method according to another embodiment of the present application, and specifically, the method may include:

step S310: environmental sounds are collected based on the audio collection device.

Step S320: based on the preset frequency range, dividing the preset frequency range into a plurality of frequency bands to be analyzed according to octaves.

Step S330: and determining the corresponding band-pass filter according to the upper limit frequency value and the lower limit frequency value of each frequency band.

Step S340: and carrying out band-pass filtering processing on the noise signal of each frequency band in the time domain based on the band-pass filter corresponding to each frequency band, and obtaining the noise signal after filtering as the noise signal to be analyzed.

Step S350: sound energy values of a noise signal to be analyzed in a plurality of frequency bands are acquired.

Step S360: the frequency band having the highest sound energy value is determined as a candidate frequency band from among the plurality of frequency bands.

Based on the obtained sound energy values of the noise signal in different frequency bands, the spectral characteristics of the noise signal can be further identified through the magnitude relation between the different energy values. In this embodiment, the frequency band having the highest sound energy value may be determined as the candidate frequency band from among the plurality of frequency bands, and since the noise signal is generally concentrated in the frequency band having the highest sound energy value, it may be determined whether the candidate frequency band is sufficient for determining the target noise reduction parameter.

In one embodiment, the noise signal may be smoothed after the bandpass filtering process. In one example, if the noise energy value of the noise signal after the smoothing process is a in the range of 100Hz to 200Hz in the first frequency band, B in the range of 200Hz to 400Hz in the second frequency band, and C in the range of 400Hz to 1000Hz in the third frequency band, if a > B > C, the first frequency band 100Hz to 200Hz corresponding to a may be determined as a candidate frequency band, and a is a corresponding candidate sound energy value.

Step S370: and determining whether the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of other frequency bands exceeds a preset ratio.

In some embodiments, if the preset ratio includes a first preset ratio and a second preset ratio, the step S370 may include steps S371 to S373, specifically, referring to fig. 9, fig. 9 shows a schematic flow chart of the step S370 in fig. 8 provided in an exemplary embodiment of the present application, in which the step S370 may include:

step S371: a first ratio of the sound energy value of the candidate frequency band to the sound energy value of the first frequency band is determined.

Step S372: a second ratio of the sound energy value of the candidate frequency band to the sound energy value of the second frequency band is determined.

The other frequency bands may include a continuous first frequency band and a continuous second frequency band, which means that the first frequency band, the second frequency band and the candidate frequency band are continuous in frequency, for example, the candidate frequency band is 100 Hz-200 Hz, the first frequency band may be 200 Hz-400 Hz, and the second frequency band may be 400 Hz-1000 Hz; for another example, the candidate frequency band may be 200 Hz-400 Hz, the first frequency band may be 100 Hz-200 Hz, and the second frequency band may be 400 Hz-1000 Hz. Thus, by comparing the sound energy value of the candidate frequency band with the sound energy values of the first frequency band and the second frequency band, the degree of difference between the candidate sound energy value of the candidate frequency band and the sound energy values of the other frequency bands can be determined.

In some possible embodiments, the first frequency band, the second frequency band and the candidate frequency band may also be contiguous in frequency, such as the candidate frequency band being 100Hz to 200Hz, the first frequency band may be 250Hz to 450Hz, and the second frequency band may be 500Hz to 1000Hz.

In some embodiments, the first preset ratio and the second preset ratio may be determined according to actual needs, may be preset by a program, or may be user-defined, which is not limited in this embodiment. In addition, the first preset ratio and the second preset ratio may be the same or different.

In one embodiment, the first preset ratio is the same as the second preset ratio, and it is determined whether the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy value of each of the other frequency bands exceeds the first preset ratio or the second preset ratio, and when the ratio exceeds the first preset ratio, it is determined that the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy value of the other frequency bands exceeds the preset ratio.

As another embodiment, the first preset ratio and the second preset ratio may be different, for example, different first preset ratio and second preset ratio may be set according to the frequency difference between the first frequency band and the second frequency band, respectively, from the candidate frequency band, and in one example, the larger the frequency difference, the larger the preset ratio may be. In another example, the larger the frequency difference, the smaller the preset ratio, and the target noise reduction parameter may be determined only by sufficiently differing the sound energy values of the candidate frequency band and the frequency band nearest thereto.

The frequency difference may be a difference between the center frequencies of the first and second frequency bands and the candidate frequency band, or a difference between the upper limit frequency or the lower limit frequency of the first and second frequency bands and the candidate frequency band, and is not limited herein. For example, if the candidate frequency band is 100Hz to 200Hz, the first frequency band may be 200Hz to 400Hz, and the second frequency band may be 400Hz to 1000Hz, the frequency difference between the first frequency band and the candidate frequency band is the difference between the center frequencies, i.e., 300Hz to 150 hz=150 Hz, and the frequency difference between the second frequency band and the candidate frequency band is the difference between the center frequencies, i.e., 700Hz to 150 hz=550 Hz, and the frequency difference corresponding to the second frequency band is greater than the frequency difference corresponding to the first frequency band.

In some embodiments, a mapping relationship between different frequency difference intervals and preset ratios may be preset, and then the corresponding preset ratio may be determined according to the frequency difference, so as to be used as the first preset ratio or the second preset ratio.

Step S373: if the first ratio exceeds the first preset ratio and the second ratio exceeds the second preset ratio, judging that the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of other frequency bands exceeds the preset ratio.

If the first ratio exceeds the first preset ratio and the second ratio exceeds the second preset ratio, the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of other frequency bands is judged to exceed the preset ratio, and the difference degree between the candidate frequency band and the other frequency bands is large enough at the moment, so that the noise signal to be analyzed can be determined as a noise type.

In some embodiments, if the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of the other frequency bands does not exceed the preset ratio, there may be a mixture of multiple noise signals, and then the frequency band with the highest sound energy value may not be included in the frequency bands divided in advance, so that the difference of the sound energy values between the frequency bands is not large enough, at this time, the frequency band with the highest sound energy value may be widened, and the other frequency bands with the ratio not exceeding the preset ratio and the candidate frequency bands are combined into one target frequency band, so that the corresponding noise reduction parameter may be generated as the target noise reduction parameter for the combined target frequency band. Therefore, the method can realize targeted noise reduction processing on a scene in which a plurality of high noise signals are mixed, and obtain better noise reduction effect.

Step S380: and if the noise reduction parameters are exceeded, determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters.

For example, if the sound energy value of the noise signal to be analyzed is a in the range of 100Hz to 200Hz in the first frequency band, the sound energy value of the noise signal to be analyzed is B in the range of 200Hz to 400Hz in the second frequency band, the sound energy value of the noise signal to be analyzed is C in the range of 400Hz to 1000Hz in the third frequency band, if a is greater than a certain multiple of B and a is greater than a certain multiple of C, i.e., the first frequency band 100Hz to 200Hz corresponding to a is a candidate frequency band, the second frequency band 200Hz to 400Hz may be marked as the first frequency band, the third frequency band 400Hz to 1000Hz may be marked as the second frequency band, the first ratio of a to B is greater than the first preset ratio, and the second ratio of a to C is greater than the second preset ratio, the noise signal to be analyzed may be classified as a noise type, and the noise reduction parameter may be adjusted to have the best noise reduction performance for the candidate frequency bands 100Hz to 200Hz at this time, thereby performing more targeted noise reduction.

Step S390: and denoising the environmental sound based on the target denoising parameter.

According to the noise reduction processing method provided by the embodiment, on the basis of the embodiment, whether the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of other frequency bands exceeds the preset ratio is determined, and when the ratio exceeds the preset ratio, the noise reduction parameters corresponding to the candidate frequency band are determined to be target noise reduction parameters, so that whether the difference degree between the candidate sound energy value and the sound energy values of other frequency bands is large enough can be determined, when the ratio exceeds the preset ratio, the difference degree is considered to be large enough, and when the difference degree is large enough, the noise reduction parameters corresponding to the candidate frequency band, namely the noise reduction parameters with noise reduction performance mainly concentrated on the candidate frequency band, are determined to be target noise reduction parameters, so that more accurate noise reduction can be realized for various noise signals, and the noise reduction effect is improved.

Referring to fig. 10, a block diagram illustrating a noise reduction processing apparatus 1000 according to an embodiment of the present application may be applied to an electronic device, where the electronic device may be the terminal or the earphone, and specifically, the noise reduction processing apparatus 1000 may include: an audio acquisition module 1010, a preprocessing module 1020, an energy acquisition module 1030, a parameter determination module 1040, and a noise reduction processing module 1050, specifically:

An audio collection module 1010 for collecting an ambient sound based on the audio collection device, the ambient sound comprising a noise signal;

the preprocessing module 1020 is configured to preprocess the collected environmental sound to obtain a noise signal to be analyzed, where the noise signal to be analyzed corresponds to a plurality of frequency bands;

an energy obtaining module 1030, configured to obtain sound energy values of the noise signal to be analyzed in a plurality of frequency bands;

a parameter determining module 1040, configured to determine corresponding target noise reduction parameters according to a proportional relationship between sound energy values of the plurality of frequency bands;

the noise reduction processing module 1050 is configured to perform noise reduction processing on the environmental sound based on the target noise reduction parameter.

Further, the parameter determination module 1040 may include: the first candidate determination submodule, the first candidate comparison submodule and the first candidate noise reduction submodule, wherein:

a first candidate determination submodule for determining a frequency band with the highest sound energy value from the plurality of frequency bands as a candidate frequency band;

the first candidate comparison sub-module is used for determining whether the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of other frequency bands exceeds a preset ratio;

And the first candidate noise reduction submodule is used for determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters if the noise reduction parameters exceed the target noise reduction parameters.

Further, the noise reduction processing apparatus 1000 may further include:

the target frequency band determining module is used for merging other frequency bands with the ratio not exceeding a preset ratio with the candidate frequency bands to be used as target frequency bands if the target frequency bands are not exceeded;

and the target noise reduction module is used for determining the noise reduction parameters corresponding to the target frequency band as target noise reduction parameters.

Further, the other frequency bands include a first frequency band and a second frequency band that are continuous, the preset ratio includes a first preset ratio and a second preset ratio, and the candidate comparison submodule includes: a first ratio determining unit, a second ratio determining unit, and a ratio comparing unit, wherein:

a first ratio determining unit configured to determine a first ratio of a sound energy value of the candidate frequency band to a sound energy value of the first frequency band;

a second ratio determining unit for determining a second ratio of the sound energy value of the candidate frequency band to the sound energy value of the second frequency band;

and the ratio comparison unit is used for judging that the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy value of other frequency bands exceeds the preset ratio if the first ratio exceeds the first preset ratio and the second ratio exceeds the second preset ratio.

Further, the parameter determination module 1040 may include: a second candidate determination submodule and a second candidate noise reduction submodule, wherein:

a second candidate determination submodule, configured to determine, from among the plurality of frequency bands, a frequency band with a highest sound energy value as a candidate frequency band if a ratio between sound energy values of the plurality of frequency bands matches a preset ratio;

and the second candidate noise reduction submodule is used for determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters.

Further, the preprocessing module 1020 may include: the frequency band dividing sub-module, the filter determining sub-module and the time filtering sub-module, wherein:

the frequency band dividing sub-module is used for dividing the preset frequency range into a plurality of frequency bands to be analyzed according to octaves based on the preset frequency range;

the filter determining submodule is used for determining a corresponding band-pass filter according to the upper limit frequency value and the lower limit frequency value of each frequency band;

and the time domain filtering sub-module is used for carrying out band-pass filtering processing on the noise signal of each frequency band based on the band-pass filter corresponding to each frequency band in the time domain to obtain a filtered noise signal as a noise signal to be analyzed.

Further, the preprocessing module 1020 may include: drop sampling submodule and noise acquisition submodule, wherein:

the downsampling submodule is used for downsampling the acquired environmental sound to obtain downsampled environmental sound;

and the noise acquisition sub-module is used for acquiring a noise signal to be analyzed according to the down-sampled environmental sound.

Further, the preprocessing module 1020 may include: a model determination sub-module and a parameter determination sub-module, wherein:

the model determining submodule is used for determining the noise type of the noise signal to be analyzed according to the proportional relation of the sound energy values of the frequency bands based on the trained deep learning model;

and the parameter determination submodule is used for determining the noise reduction parameter corresponding to the noise type as a target noise reduction parameter.

Further, the noise reduction processing apparatus 1000 further includes: a smoothing processing module, wherein:

and the smoothing processing module is used for carrying out smoothing processing on sound energy values of the noise signal to be analyzed in a plurality of frequency bands.

Further, the pre-processing module 1020 may include: a noise reducing promoter module, wherein:

and the noise reduction promoter module is used for preprocessing the environmental sound to obtain a noise signal to be analyzed if the sound energy value of the noise signal in the environmental sound exceeds a preset energy value.

The noise reduction processing device provided in the embodiment of the present application is used to implement the corresponding noise reduction processing method in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein again.

In several embodiments provided herein, the coupling of the modules to each other may be electrical, mechanical, or other.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.

Referring to fig. 11, a block diagram of an electronic device according to an embodiment of the present application is shown. The electronic device 1100 may be a terminal capable of running an application program, such as a headset or smart phone, a tablet computer, an MP3 player, an MP4 player, an electronic book, a notebook computer, a personal computer, a wearable electronic device, etc. The electronic device 1100 in the present application may include one or more of the following components: a processor 1110, a memory 1120, and one or more application programs, wherein the one or more application programs may be stored in the memory 1120 and configured to be executed by the one or more processors 1110, the one or more program(s) configured to perform the method as described in the foregoing method embodiments.

Processor 1110 may include one or more processing cores. The processor 1110 utilizes various interfaces and lines to connect various portions of the overall electronic device 1100, perform various functions of the electronic device 1100, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1120, and invoking data stored in the memory 1120. Alternatively, the processor 1110 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1110 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for being responsible for rendering and drawing of display content; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1110 and may be implemented solely by a single communication chip.

The Memory 1120 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Memory 1120 may be used to store instructions, programs, code, sets of codes, or instruction sets. The memory 1120 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described below, etc. The storage data area may also store data created by the electronic device 1100 in use (e.g., phonebook, audiovisual data, chat log data), and the like.

Referring to fig. 12, a block diagram of a headset according to an embodiment of the present application is shown. The headset 1200 may include an audio acquisition device 1210, an audio output device 1220, and an audio signal processing circuit 1230. Wherein:

the audio collection device 1210 is configured to collect environmental sounds. In some embodiments, the audio acquisition device 1210 may be a microphone or other device capable of acquiring audio signals for acquisition and transmission to the audio signal processing circuit 1220.

The audio signal processing circuit 1220 is configured to obtain an environmental sound collected by the audio collection device; preprocessing the environmental sound to obtain a noise signal to be analyzed, wherein the noise signal to be analyzed corresponds to a plurality of frequency bands; acquiring sound energy values of the noise signals to be analyzed in a plurality of frequency bands; and determining corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the frequency bands.

The audio output device 1230 is configured to output an audio signal, and in one embodiment, the audio output device 1230 may perform noise reduction processing on the environmental sound based on the target noise reduction parameter. In some implementations, the audio output device 1230 may be a speaker or other device that can output audio signals.

In addition, in some embodiments, the earphone 1200 may further include a power supply circuit, where the power supply circuit may supply power to other hardware components, and the power supply source may be a battery built in the earphone 1200, an external power input, or a power generating device built in the earphone 1200.

The earphone 1200 provided in the embodiment of the present application is used to implement the corresponding noise reduction processing method in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein again.

Referring to fig. 13, a block diagram of a computer readable storage medium according to an embodiment of the present application is shown. The computer readable storage medium 1300 has stored therein program code that can be invoked by a processor to perform the methods described in the above embodiments.

The computer readable storage medium 1300 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Optionally, computer readable storage medium 1300 includes non-volatile computer readable storage medium (non-transitory computer-readable storage medium). The computer readable storage medium 1300 has storage space for program code 1310 that performs any of the method steps described above. The program code can be read from or written to one or more computer program products. Program code 1310 may be compressed, for example, in a suitable form.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, one of ordinary skill in the art will appreciate that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not drive the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A noise reduction processing method, the method comprising:

acquiring environmental sounds acquired by an audio acquisition device, wherein the environmental sounds comprise noise signals;

preprocessing the environmental sound to obtain a noise signal to be analyzed, wherein the noise signal to be analyzed corresponds to a plurality of frequency bands;

acquiring sound energy values of the noise signals to be analyzed in a plurality of frequency bands;

determining a frequency band having the highest sound energy value from the plurality of frequency bands as a candidate frequency band;

determining whether the ratio of the sound energy values of the candidate sound energy values corresponding to the candidate frequency bands to the sound energy values of other frequency bands exceeds a preset ratio, wherein partial frequency bands which are not overlapped with the candidate frequency bands exist;

if the noise reduction parameters are all exceeded, determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters;

if the ratio is not exceeded, combining other frequency bands with the ratio which is not exceeded by the preset ratio with the candidate frequency band to serve as a target frequency band;

determining the noise reduction parameters corresponding to the target frequency bands as target noise reduction parameters;

and carrying out noise reduction processing on the environmental sound based on the target noise reduction parameter.

2. The method of claim 1, wherein the other frequency bands include a first frequency band and a second frequency band that are consecutive, the preset ratio includes a first preset ratio and a second preset ratio, and the determining whether the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy value of the other frequency band exceeds the preset ratio includes:

Determining a first ratio of sound energy values of the candidate frequency bands to sound energy values of the first frequency band;

determining a second ratio of the sound energy value of the candidate frequency band to the sound energy value of the second frequency band;

if the first ratio exceeds a first preset ratio and the second ratio exceeds a second preset ratio, judging that the ratio of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of other frequency bands exceeds the preset ratio.

3. The method according to claim 1 or 2, wherein the preprocessing of the ambient sound to obtain a noise signal to be analyzed comprises:

dividing a preset frequency range into a plurality of frequency bands to be analyzed according to octaves based on the preset frequency range;

determining a corresponding band-pass filter according to the upper limit frequency value and the lower limit frequency value of each frequency band;

and carrying out band-pass filtering processing on the noise signal of each frequency band in the time domain based on the band-pass filter corresponding to each frequency band, and obtaining the noise signal after filtering as the noise signal to be analyzed.

4. The method of claim 1, wherein the preprocessing the ambient sound to obtain a noise signal to be analyzed comprises:

Performing downsampling processing on the acquired environmental sound to obtain downsampled environmental sound;

and obtaining a noise signal to be analyzed according to the down-sampled environmental sound.

5. The method of claim 1, wherein the acquiring the noise signal to be analyzed is followed by sound energy values for a plurality of frequency bands, the method further comprising:

and smoothing the sound energy values of the noise signal to be analyzed in a plurality of frequency bands.

6. The method of claim 1, wherein the preprocessing the ambient sound to obtain a noise signal to be analyzed comprises:

if the sound energy value of the noise signal in the environmental sound exceeds the preset energy value, preprocessing the environmental sound to obtain the noise signal to be analyzed.

7. A noise reduction processing apparatus, characterized in that the apparatus comprises:

the audio acquisition module is used for acquiring the environmental sound acquired by the audio acquisition device, wherein the environmental sound comprises a noise signal;

the preprocessing module is used for preprocessing the environmental sound to obtain a noise signal to be analyzed, and the noise signal to be analyzed corresponds to a plurality of frequency bands;

The energy acquisition module is used for acquiring sound energy values of the noise signal to be analyzed in a plurality of frequency bands;

a parameter determination module configured to determine a frequency band having a highest sound energy value from the plurality of frequency bands as a candidate frequency band; determining whether the ratio of the sound energy values of the candidate sound energy values corresponding to the candidate frequency bands to the sound energy values of other frequency bands exceeds a preset ratio, wherein partial frequency bands which are not overlapped with the candidate frequency bands exist; if the noise reduction parameters are all exceeded, determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters; if the ratio is not exceeded, combining other frequency bands with the ratio which is not exceeded by the preset ratio with the candidate frequency band to serve as a target frequency band; determining the noise reduction parameters corresponding to the target frequency bands as target noise reduction parameters;

and the noise reduction processing module is used for carrying out noise reduction processing on the environmental sound based on the target noise reduction parameters.

8. An earphone, characterized by including audio acquisition device, audio output device and audio signal processing circuit, wherein:

the audio acquisition device is used for acquiring environmental sounds;

the audio signal processing circuit is used for acquiring the environmental sound acquired by the audio acquisition device; preprocessing the environmental sound to obtain a noise signal to be analyzed, wherein the noise signal to be analyzed corresponds to a plurality of frequency bands; acquiring sound energy values of the noise signals to be analyzed in a plurality of frequency bands; determining a frequency band having the highest sound energy value from the plurality of frequency bands as a candidate frequency band; determining whether the ratio of the sound energy values of the candidate sound energy values corresponding to the candidate frequency bands to the sound energy values of other frequency bands exceeds a preset ratio, wherein partial frequency bands which are not overlapped with the candidate frequency bands exist; if the noise reduction parameters are all exceeded, determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters; if the ratio is not exceeded, combining other frequency bands with the ratio which is not exceeded by the preset ratio with the candidate frequency band to serve as a target frequency band; determining the noise reduction parameters corresponding to the target frequency bands as target noise reduction parameters;

The audio output device is used for carrying out noise reduction processing on the environmental sound based on the target noise reduction parameter.

9. An electronic device, comprising:

one or more processors;

a memory;

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-6.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program code, which is callable by a processor for executing the method of any one of the claims 1-6.