CN113949955A

CN113949955A - Noise reduction processing method and device, electronic equipment, earphone and storage medium

Info

Publication number: CN113949955A
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: 2022-01-18
Anticipated expiration: 2040-07-16
Also published as: CN113949955B

Abstract

The application discloses a noise reduction processing method, a noise reduction processing device, electronic equipment, earphones 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 signal 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 multiple frequency bands; and performing noise reduction processing on the environmental sound based on the target noise reduction parameter. According to the embodiment of the application, the sound energy values of the noise signals in a plurality of frequency bands are identified, the target noise reduction parameters are determined according to the proportional relation between the sound energy values and the target noise reduction parameters, and targeted noise reduction processing is carried out on the basis of the target noise reduction parameters, so that a better active noise reduction effect can be obtained for the noise signals of various frequency bands in daily use.

Description

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

Technical Field

The present application relates to the field of noise reduction technologies, and in particular, to a noise reduction processing method and apparatus, 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 due to different noise compositions in different environments, ambient noise is eliminated by the fixed filter parameters, and when the noise in the ambient environment changes remarkably, the noise reduction effect of the active noise reduction is not stable enough. For example, in an active noise reduction earphone with a noise reduction peak value of 80-200 Hz, the active noise reduction performance is significantly reduced between 400-2000 Hz, and a part of 400-2000 Hz noise in the daily ambient noise cannot be effectively eliminated, i.e., the noise reduction effect of the existing active noise reduction earphone is not good.

Disclosure of Invention

The embodiment of the application provides a noise reduction processing method and device, electronic equipment, an earphone and a storage medium, and 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 signal 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 multiple frequency bands; and performing 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 environmental sounds acquired by the audio acquisition device, and the environmental sounds comprise noise signals; 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 multiple frequency bands; and the noise reduction processing module is used for carrying out noise reduction processing on the environment 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 application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the noise reduction processing method provided by the first aspect. In a fourth aspect, an embodiment of the present application provides 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 signal 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 multiple frequency bands; and the audio output device is used for carrying out noise reduction processing on the environment sound based on the target noise reduction parameter.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where a program code is stored in the computer-readable storage medium, and the program code may be called by a processor to execute the noise reduction processing method provided in the first aspect.

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

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 shows an active noise reduction principle.

Fig. 2 shows a schematic diagram of an application environment suitable for the embodiment of the present application.

Fig. 3 shows a flowchart of a noise reduction processing method according to an embodiment of the present application.

Fig. 4 shows a flowchart of a noise reduction processing method according to another embodiment of the present application.

Fig. 5 illustrates a flowchart 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 by 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 flowchart illustrating a noise reduction processing method according to yet another embodiment of the present application.

Fig. 9 illustrates a flowchart 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 a program code for implementing the noise reduction processing method according to the embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

Most of the Active Noise reduction (ANC) earphones are designed with a fixed Active Noise reduction parameter to realize a fixed Active Noise reduction Curve (Active Noise reduction Level current) based elimination of external environment Noise, and the technical principle of the conventional Active Noise reduction (ANC) earphones is as shown in fig. 1. 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 (ANC): the principle of the method is that according to noise sound waves picked up at a specified position, a loudspeaker is used for playing sound, and sound waves with opposite phases and the same amplitude as original sound waves are generated at the specified position. Since a sound wave is a mechanical vibration, when two sound waves meet in space, linear superposition occurs. The secondary sound field generated by the loudspeaker playing and the original noise field meet at the designated position and are linearly superposed, and the superposed two sound waves with the same amplitude and opposite phases can cancel each other, so that the noise is weakened or even eliminated, and a listener can obtain a quieter listening experience.

Active noise reduction curve: the active noise reduction curve is a curve of the active noise reduction amount of the noise reduction device changing along with the frequency, and is used for showing the noise reduction capability of the device at different frequencies of sound. The concrete embodiment is that the vertical axis is the curve of the active noise reduction quantity and the horizontal axis is the curve of the frequency. The noise reduction amount of the noise reduction means represents the degree to which audible sound waves are reduced before reaching the eardrum of a person. The noise reduction amount of the active noise reduction on the sound waves with different frequencies is different, and compared with a high-frequency signal, 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 amount values of the frequency points is called a noise reduction curve, and the noise reduction capability of different frequency points is accurately described.

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

However, if the ambient noise is eliminated by using a fixed active noise reduction curve, the noise reduction experience of the ANC headset is unstable when the noise of the ambient environment changes significantly. For example, an ANC earphone with a noise reduction performance peak value at 80-250 Hz has a good active noise reduction experience when the ambient noise is mainly low-frequency noise at 80-250 Hz, but the active noise reduction experience is significantly deteriorated when a user wears the earphone into an environment where the ambient noise is mainly low-frequency noise at 250-400 Hz. Ultimately leading to significant differences in the active noise reduction experience in different environments when the user uses a typical ANC headset.

In addition, a small part of ANC earphones can weaken noise of a part of frequency bands according to the characteristics of the surrounding environment. For example, when such an ANC headset is used in conjunction with its handset side APP, the pass-through mode (i.e. the external ambient noise is amplified to the ear, so that the headset wearer can hear the external ambient noise more clearly, which function is approximately handled in the opposite direction of noise reduction) can be set to the "speech-related" mode. In the speech mode, the earphone will have 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 earphone. However, this process still treats all different ambient noises identically by means of a fixed active noise reduction curve, and the above-mentioned problems still exist.

In addition, some ANC earphones switch noise reduction modes with different noise reduction intensity gears according to the overall intensity of the environmental noise, but no matter the switching is realized automatically or manually, different frequency components in the noise cannot be flexibly distinguished and processed.

Based on the above problems, embodiments of the present application provide a noise reduction processing method and apparatus, an electronic device, and a computer-readable storage medium, which identify a spectral feature of an environmental noise by processing and analyzing an acquired environmental noise signal, and adjust a noise reduction parameter according to the spectral feature of the noise, so as to obtain an optimal noise reduction effect on the noise. In order to better understand 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 is a schematic diagram illustrating an application environment suitable for the embodiment of the present application. The noise reduction processing method provided by the embodiment of the application can be applied to the noise reduction processing system 10 shown in fig. 2. The noise reduction processing system 10 includes a terminal 100 and an earphone 200.

The terminal 100 may be, but not limited to, a mobile phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer iii, motion video compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer iv, motion video compression standard Audio Layer 4), 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 the noise reduction mode of the headset may be installed in the terminal 100. The terminal 100 may transmit the noise reduction mode to be implemented to the headset 200, and the headset 200 plays audio, which may include inverse sound waves, music signals, masking sounds, and the like. The terminal 100 may include an audio acquisition device for acquiring ambient sound, and may be provided with a processor for executing the noise reduction processing method provided in the embodiment of the present application, and then correspondingly playing audio through the earphone 200 to implement noise reduction.

The terminal 100 and the earphone 200 may be connected through a wired or wireless connection, and optionally, if the terminal 100 and the earphone 200 are connected through a wireless connection, the terminal 100 and the earphone 200 may be connected through a wireless connection based on Bluetooth (Bluetooth), a 2.4G wireless communication technology, an infrared transmission technology, or a wireless network, so as to implement data transmission, for example, after the earphone 200 and the terminal 100 are connected through a wireless connection, sound source data may be obtained through the terminal 100 for playing. Alternatively, the Wireless network may be a mobile communication network or a Wireless Fidelity (WiFi) network.

The earphone 200 may be a wired earphone or a wireless earphone. Optionally, the headset 200 may also be embodied as a true wireless headset. In practice, those skilled in the art may select other types of earphones to implement the scheme, 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 only include the earphone 200, that is, the noise reduction processing method provided in the embodiment of the present application may also be implemented without a terminal, for example, in a scene where the earphone 200 does not play music but 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 embodiment of the present application may be implemented separately.

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

In some embodiments, each of the earphones 200 may include an audio capture device, an audio output device, and an audio signal processing circuit, and specifically 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 in addition, the earphones 200 may further 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 configured as mono headphones or as true wireless headphones, each headphone 200 has at least 1 speaker. When the earphones 200 are configured as a two-channel earphone or a multi-channel earphone, each earphone 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, and the picked-up ambient sounds are used 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 reverse noise; the noise detection analyzes the desired ambient noise signal. The two purposes can be realized by 1 microphone, the hardware cost is saved, and the two purposes can also be realized by a plurality of microphones respectively.

Among other things, the audio signal processing circuit can be used for two purposes: the ANC denoising function is used for determining denoising parameters for denoising and sending the denoising parameters to the loudspeaker to output corresponding inverse sound waves so as to realize denoising; 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 headset 200, may be an external power input, or may be a power generating device built in the headset 200.

The noise reduction processing method, apparatus, electronic device, earphone and storage medium provided in the embodiments of the present application will be described in detail below with specific embodiments.

Referring to fig. 3, fig. 3 is a schematic flowchart illustrating a noise reduction processing method according to an embodiment of the present application, and the method is applicable to an electronic device, where the electronic device may be the terminal or the earphone. The flow diagram shown in fig. 3 will be described in detail below. 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 an 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 and also can be arranged at the earphone, and environmental sounds are acquired based on the audio acquisition device. In some embodiments, the audio collecting device may be a microphone or other device that can be used to collect sound signals, and is not limited herein.

Taking the example of the audio acquisition device being disposed on the earphone, the audio acquisition device based on the earphone, such as a microphone, can acquire the environmental sound. When the microphone is arranged on the earphone, the cost can be saved, and the external environment sound signal picked up by the multiplexing feedforward microphone can be used for feedforward noise reduction design and can also be used for picking up the input voice of a user without generating extra hardware cost.

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

In some embodiments, the electronic device obtains the environmental sound collected by the audio collecting device, and if the environmental sound includes a noise signal, the active noise reduction function may be started, and step S120 and the following steps are performed.

In other embodiments, after acquiring the environmental sound collected by the audio collection device, the electronic device may start the active noise reduction function only if the sound energy value of the noise signal in the environmental sound exceeds the preset energy value, and execute step S120 and subsequent steps to perform noise reduction processing on the noise, and if the sound energy value of the noise signal in the environmental sound does not exceed the preset energy value, the step S120 and subsequent steps may not be performed, for example, the environmental sound may be monitored continuously or monitoring may be terminated, so that the active noise reduction processing may not be performed when the noise signal in the environmental sound is weak and has little influence on the user, power consumption of the device may be reduced, and resources may be saved.

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

In some embodiments, the pre-processing the acquired environmental sound may include performing analog-to-digital conversion on the acquired environmental sound to obtain a digital signal, and performing pre-emphasis, framing, windowing, Mel-Frequency Cepstral Coefficients (MFCCs) 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 treatment steps than those described above, and is not limited herein.

In this embodiment, the acquired environmental sounds are preprocessed, and the noise signal to be analyzed may be divided into a plurality of frequency bands. Specifically, the frequency band refers to a 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 600 Hz.

Step S130: and acquiring sound energy values of the noise signal to be analyzed in a plurality of frequency bands.

After obtaining the noise signal to be analyzed, sound energy values of the noise signal to be analyzed in a plurality of frequency bands may be obtained.

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

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, the corresponding target noise reduction parameters can be determined according to the proportional relation among the sound energy values of the multiple frequency bands of the noise signal, so that the noise reduction processing is performed on the noise signal in the environmental sound based on the target noise reduction parameters. By obtaining the proportional relation among the sound energy values of the multiple frequency bands, the frequency bands with more concentrated sound energy values can be more accurately determined, so that the noise types can be more accurately distinguished, more accurate noise reduction parameters can be determined, and better noise reduction effect can be achieved.

For example, in some embodiments, a plurality of groups of preset noise reduction parameters for various noise signals may be constructed in advance, and a mapping relationship between each preset noise reduction parameter and a proportional relationship is constructed, so that according to the proportional relationship between the sound energy values of a plurality of frequency bands, the corresponding preset noise reduction parameter is determined as the target noise reduction parameter, so that noise reduction processing is performed on the environmental sound based on the target noise reduction parameter, thereby reducing the amount of computation, and if the embodiment is applied to the earphone, the power consumption of the earphone can be reduced, and the endurance time of the earphone can be improved.

In some embodiments, the noise type of the noise signal to be analyzed may be determined based on a trained neural network model according to a proportional relationship between energy values of a plurality of frequency bands, and then a noise reduction parameter corresponding to the noise type is determined as a target noise reduction parameter, so that the neural network model may use the collected noise signals of various frequency bands as training samples and mark the noise type of each training sample, and thus the trained neural network model obtained by training the 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 also be determined as the target noise reduction parameter by comparing the sound energy values of the multiple 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 other frequency bands. The following embodiments may be mentioned, and are not described herein.

In other embodiments, the proportional relationship between the sound energy values of the multiple frequency bands may also be directly determined by the proportion between the sound energy values of the multiple frequency bands, and specifically, if the proportion between the sound energy values of the multiple frequency bands matches a preset proportion, a frequency band with the highest sound energy value among the multiple frequency bands may be determined as a candidate frequency band, and a 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., without limitation. In addition, matching with the preset ratio may be an exact matching or an approximate matching, for example, the ratio between the sound energy values of three frequency bands is 1.1: 1: 2, presetting the proportion of 1: 1: 2, at this time, it can also be determined that the proportions of the three frequency bands match the preset proportions, 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 proportions of the plurality of frequency bands are matched with the preset proportions, that is, as long as the proportions of the plurality of frequency bands can be matched with the preset proportions.

In one 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 can be determined that the ratio among 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 can be determined as the target noise reduction parameter.

Step 150: and carrying out noise reduction processing on the environmental sound based on the target noise reduction parameters.

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 inverse sound wave may be output by the earphone based on the target noise reduction parameter to perform noise reduction processing on the ambient sound.

The noise reduction processing method provided by the embodiment of the application acquires the environmental sound based on the audio acquisition device, wherein the environmental sound may contain noise signals, then preprocesses the acquired environmental sound to obtain the noise signals to be analyzed corresponding to a plurality of frequency bands, then acquires the sound energy values of the noise signals to be analyzed in the plurality of frequency bands, determines corresponding target noise reduction parameters according to the proportional relation among the sound energy values of the plurality of frequency bands, and finally performs noise reduction processing on the environmental sound based on the noise reduction parameters. Therefore, the sound energy values of the noise signals in a plurality of frequency bands are identified, the target noise reduction parameters are determined according to the proportional relation between the sound energy values and the target noise reduction parameters, and targeted noise reduction processing is carried out on the basis of the target noise reduction parameters, so that the noise signals of various frequency bands in daily use can obtain a better active noise reduction effect.

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

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

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

Since the resolution of the ear listening system to the sound frequency is not fixed, the higher the frequency is, the lower the frequency resolution of the ear listening system is, and the frequency interval that the ear listening system can distinguish is approximately in a logarithmic direct relation with 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 as a logarithmic frequency band, such as an octave. In some embodiments, if the noise signal is to be identified with finer granularity, the analysis bandwidth may be set to 1/2 octaves or 1/3 octaves, which is not limited in this embodiment.

In some embodiments, the preset frequency range may be determined according to actual needs, for example, considering that most ANC earphones have a weak noise reduction effect in a frequency band of 1-2 kHz, and may analyze only low-frequency noise components with frequencies below 1kHz, the preset frequency range may be a 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 a nonlinear working area in a strong low-frequency vibration field and further generating abnormal sound, the noise reduction effect of the ANC earphone below 100Hz is generally poor. Therefore, the first band to be analyzed can be set to 100 to 200Hz, and the noise signal can be analyzed in logarithmic frequency range by combining the frequency resolution performance of the human auditory system, and the second band to be analyzed can be set to 200 to 400 Hz. In addition, as an embodiment, the third frequency band to be analyzed may be set to 400 to 800Hz according to the log octave characteristic.

In another embodiment, considering that most ANC earphones have a weak noise reduction effect in a frequency band of 1 to 2kHz, and only low-frequency noise components with frequencies below 1kHz can be analyzed, only frequency spectrum components below 1kHz can be considered, and a third frequency band to be analyzed can be set to 400 to 1000 Hz. Thus, the frequency range of different frequency bands to be analyzed can be determined in combination with the frequency characteristics of the active noise reduction and the characteristics of the human auditory system.

It should be noted that, the noise reduction performance of different ANC earphones is different, and the third frequency band to be analyzed can be flexibly set according to the noise reduction performance of the ANC earphones in different frequency bands, for example, the highest frequency at which the noise reduction performance starts 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 starts to deteriorate in the frequency band above 1.2kHz or is lower than the set threshold, the third frequency band to be analyzed may also be set to 400 to 1200Hz, and 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 another frequency, the frequency band may be divided into a plurality of narrower frequency bands, and the like, which is not limited in the present embodiment. The log-frequency-range analysis may be based on an octave, 1/2 octave, 1/3 octave, or the like, which is not limited in this embodiment.

It is considered that the resolving power of a human ear listening system for sound frequencies has a logarithmic characteristic, i.e. the resolution for the low frequency part is high, but the resolution for the high frequency part is low instead. Therefore, compared with a noise reduction method for performing fourier transform on noise signals acquired by an audio acquisition device and then calling different noise reduction modes by analyzing noise spectrum differences, the embodiment identifies noise by setting a frequency band to be analyzed and combining noise spectrum characteristics in the frequency band, can more fully consider subjective listening characteristics of an ear listening system, reduces consideration of excessive noise spectrum details, and enables noise signals to have better robustness when being processed and analyzed.

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

And 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 performing band-pass filtering processing on the noise signal of each frequency band on the basis of the band-pass filter corresponding to each frequency band in a time domain to obtain a filtered noise signal serving as the noise signal to be analyzed.

And performing 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, and filtering out signals in different frequency bands to be analyzed so as to identify the frequency spectrum characteristics of the environmental noise signal subsequently. 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 transform is not needed to be carried out on the frequency spectrum, and the filtering is only carried out on the time domain, the processing is simpler, and the characteristic of noise reduction is better met.

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

In other embodiments, the filtering may not be band-pass filtering but low-pass filtering, and is not limited herein.

In some embodiments, the preprocessing may further include down-sampling, for example, before the band-pass filtering is performed on the noise signal of each frequency band based on the band-pass filter corresponding to each frequency band in the time domain, the down-sampling may be performed on the acquired environmental sound first to obtain a down-sampled environmental sound; and obtaining a noise signal to be analyzed according to the environment sound after the down sampling. Specifically, in some embodiments, the down-sampled environmental sound is obtained, and the down-sampled environmental sound may be preprocessed to obtain a noise signal to be analyzed, where the preprocessing step may refer to the step S120, and is not described herein again. Of course, in other embodiments, when the pre-processing is performed on the down-sampled environmental sound, the filtering processing may also be performed on the noise signal of the environmental sound, and for specific embodiments, reference may be made to the foregoing steps S220 to S240, which is not described herein again.

Due to the high delay requirement of the active noise reduction processing on the system, for example, in some scenarios, the hardware delay of the system needs to be within 20 microseconds. Therefore, in the active noise reduction processing path, the sampling rate of the digital signal is high, basically above 192kHz, and even part of ANC earphones also adopt a high sampling rate of 768 kHz. However, when the noise spectrum is subjected to the identification analysis and classification processing, the sensitivity to the system delay is much lower, and the calculation amount is considered to be larger as the sampling rate is higher, so that the digital signal with a high sampling rate may be subjected to the down-sampling processing before the noise spectrum identification analysis is performed.

In some embodiments, since active noise reduction is mainly effective for frequency components below 2kHz, the noise signal in this frequency range can be identified and analyzed. To cover the frequency range below 2kHz, it is sufficient that the microphone signal sampling rate need only be no less than 4 kHz. Meanwhile, the lower the frequency, the more the calculation amount can be saved, so that the down-sampling processing can be adopted; higher sampling rates may also be used if the computational power of the electronic device is sufficient, such as the computational power of the audio signal processing circuitry. As one mode, a 16kHz sampling rate can be adopted, and the acquired environmental sound is down-sampled based on the 16kHz sampling rate; as another mode, it is also possible to adopt different sampling rates, and it is only necessary to ensure that the sampling rate is not lower than 4kHz, and the accuracy of the noise signal can be ensured while the computation amount is still reduced to a certain extent.

Step S250: and acquiring sound energy values of the noise signal to be analyzed in a plurality of frequency bands.

Through the band-pass filtering processing, the noise components of the noise signal in different frequency bands to be analyzed can be obtained, and at the moment, the sound energy value 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 multiple frequency bands, the sound energy values of the noise signal to be analyzed in the multiple frequency bands may be smoothed. Because the noise signal in the environment always changes along with time, in actual use, in order to enable the active noise reduction effect not to be frequently switched to cause poor use experience, the sound energy values of the noise signal in different frequency band ranges are subjected to smoothing processing, wherein the specific smoothing speed can be adjusted according to actual needs, can also be preset by a program, and can also be customized by a user. The tracking speed of the noise signal change can be slowed down through energy smoothing processing, 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 then step S260 may include step S261 to step S262, specifically, please refer to fig. 5, fig. 5 shows a flowchart of step S260 in fig. 4 according to an exemplary embodiment of the present application, in which step S260 may include:

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

Before training the deep learning model, a noise database may be collected, for example, noise signals in different environments may be collected based on different noise environments to obtain corresponding noise signals. In order to improve the identification 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 may be segmented based on a preset time length to obtain a plurality of segments of noise signals with a duration length of the preset time length, where the preset time length may be determined according to actual needs, or may be preset by a program or user-defined, and this embodiment is not limited thereto. In one example, the preset time duration may be set to vary from several seconds to several tens of seconds.

After the collected noise signals are segmented, the sound energy values of each segment of the noise signals in different frequency bands can be marked. In some embodiments, only the sound energy values of the noise signal at each frequency band may be labeled; in other embodiments, the proportional relationship of the sound energy values of the noise signal in a plurality of frequency bands can 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 the determination may be specifically performed according to the input and output definition when the deep learning model is constructed.

Wherein, the noise types can be divided according to various dimensions, and in some embodiments, can be divided according to the frequency band with the highest sound energy value; in other embodiments, the division may be by different noise generation scenarios; in still other embodiments, sounds such as snoring, air conditioning, speech, etc. may be divided by the way the noise is generated. The present embodiment does not limit the way of dividing the noise types.

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

In addition, in some embodiments, when determining the frequency band with the highest sound energy value from the plurality of frequency bands, it may also be determined whether the ratios of the frequency band with the highest sound energy value to the sound energy values of the other frequency bands all exceed a preset ratio, and if all the ratios exceed the preset ratio, the frequency band with the highest sound energy value is labeled as a frequency band corresponding to a noise type. If the sum of the noise reduction parameters and the noise type exceeds a preset ratio, 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 used as a frequency band corresponding to one noise type for marking, and corresponding noise reduction parameters are generated as 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 implementation manner can be seen in the following examples, which are not described herein again.

In another embodiment, since the spectral characteristics of the noise in different scenes are different and the sound energy values in different frequency bands are also different, the noise types can be divided according to the scenes, for example, the noise types can include subway noise in a subway environment, office noise in an office environment, and the like, noise signals of various noise types such as subway noise based on the subway environment, office noise based on the office environment, and the like can be collected in advance, and the corresponding noise types are labeled, when the deep learning model is trained, the spectral characteristics of the noise signals can be analyzed, for example, the proportional relation of the noise signals in a plurality of frequency bands is used as the input of the deep learning model, and the noise type corresponding to the noise signals is 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 based on the subway environment, for example, if the proportional relationship between the sound energy values of the noise signal in the multiple frequency bands matches the proportional relationship between the sound energy values of the subway noise in the corresponding frequency bands, it may be determined that the noise type of the noise signal is the subway noise, where the determination of whether the spectral features match may be implemented by a trained deep learning model, and the noise type may be determined by the trained deep learning model.

In another embodiment, since the spectral characteristics of the noise generated by different methods have great 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 good 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 improve the targeted noise reduction effect, the noise types can be divided according to the difference of the noise generation methods, such as the snore, the air-conditioning sound, the speaking sound, and the like, the noise signals of various noise types such as the snore, the air-conditioning sound, the speaking sound and the like can be collected in advance, and the corresponding noise types are labeled, when the deep learning model is trained, the foregoing description can be referred to, so that in one example, if the proportional relationship of the sound energy values of the collected noise signals in multiple frequency bands matches the proportional relationship of the sound energy values of the snore in the corresponding frequency bands, it may be determined that the noise type of the noise signal is snoring.

Establishing a deep learning model based on a neural network, training the deep learning model based on the marked noise signal, training parameters of the deep learning model, including the number of network layers, an activation function and the like, so as to obtain a desirable range of the training parameters, judging whether the 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 (CNN), a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), or the like, which is not limited herein. Therefore, the noise type of the noise signal to be analyzed can be determined according to the proportional relation of the sound energy values of the multiple frequency bands based on the trained deep learning model.

In some embodiments, the trained deep learning model may be transplanted into the audio signal processing circuit of the headset, and then the trained deep learning model may be run based on the headset. In other embodiments, the method can also be deployed in a terminal to run the trained deep learning model based on the terminal, so that the operation burden of the headset is reduced, and the power consumption of the headset is reduced. Especially when the earphone is a true wireless earphone, the endurance time can be improved.

Step S262: and determining the noise reduction parameters corresponding to the noise types as target noise reduction parameters.

In some embodiments, mapping relationships between the respective noise types and the noise reduction parameters may be stored in advance, so that the corresponding noise reduction parameters may be determined as the target noise reduction parameters according to the noise types. In other embodiments, a corresponding noise reduction parameter may also be generated in real time according to the noise type as a 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 inside the earphone, and may respectively correspond to 3 sets of active noise reduction curves, and respectively match noise reduction processes of different noise types.

Step S270: and carrying out noise reduction processing on the environmental sound based on the target noise reduction parameters.

After the noise type is determined, the corresponding target noise reduction parameter can be determined according to the noise reduction parameter corresponding to the noise type. For example, 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 the 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 and the energy in the frequency spectrum of the noise signal is mainly concentrated at 400-600 Hz, the active noise reduction curve of the ANC earphone can be adjusted to have the noise reduction performance concentrated at the 400-600 Hz frequency band, so that a 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 frequency spectrum characteristic 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, and when the spectral characteristics of the noise signal are identified as shown in fig. 6, the noise reduction parameters with the noise reduction performance mainly concentrated below 200Hz may be determined as the target noise reduction parameters, and the noise reduction processing may be performed on the ambient sound according to the target noise reduction parameters, 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 around 500Hz to 600Hz, and when the spectral characteristics of the noise signal are identified as shown in fig. 7, the noise reduction parameter whose noise reduction performance is mainly concentrated between 500Hz and 600Hz may be determined as the target noise reduction parameter, and the noise reduction processing may be performed on the ambient sound based on the target noise reduction parameter, for example, the active noise reduction curve is adjusted to have noise reduction performance concentrated around 500Hz to 600Hz, and the earphone may perform noise reduction processing based on the active noise reduction curve.

It should be noted that, for parts not described in detail in this embodiment, reference may be made to the foregoing embodiments, and details are not described herein again.

According to the noise reduction processing method provided by the embodiment, the environment sound is collected based on the audio collection device, then the corresponding band-pass filter is generated based on the multiple frequency bands to be analyzed, which are divided by the octaves, and the band-pass filtering processing is directly performed on the time domain to obtain the noise signal to be analyzed, so that the processing is simpler, and the characteristic of noise reduction is better met. Then, according to the proportional relation of the noise signal to be analyzed among the sound energy values of the multiple frequency bands, the spectral characteristics of the noise signal are identified and obtained, and then the noise reduction parameters are correspondingly adjusted according to the spectral characteristics, so that a better noise reduction effect under different noises is obtained. In addition, the acquired environmental sounds can be subjected to down-sampling before the band-pass filtering, so that the calculation amount of the earphone can be considered, and the power consumption of the earphone can be reduced. And a plurality of frequency bands to be analyzed are divided by adopting an octave which is more in line with the human ear listening system, so that the noise type can be identified by the robust subjective perception characteristic which is more in line with the human ear listening system on the premise of not considering too much noise spectrum details, the subjective hearing and noise reduction effects which are more in line with the human ear are obtained, and the subjective experience of a listener is improved. In addition, based on the noise reduction processing method provided by the embodiment, one set of ANC earphones can realize noise reduction processing on various noises in daily use, so that the number of ANC earphones used and purchased by a user is reduced.

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

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

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

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

Step S340: and performing band-pass filtering processing on the noise signal of each frequency band on the basis of the band-pass filter corresponding to each frequency band in a time domain to obtain a filtered noise signal serving as the noise signal to be analyzed.

Step S350: and acquiring sound energy values of the noise signal to be analyzed in a plurality of frequency bands.

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

Based on the obtained sound energy values of the noise signals in different frequency bands, the spectral characteristics of the noise signals can be further identified through the magnitude relation between different energy values. In this embodiment, the frequency band with the highest sound energy value from the plurality of frequency bands may be determined as a candidate frequency band, and since the noise signal is generally concentrated on the frequency band with 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, after the band pass filtering process, the noise signal may be smoothed. In an example, if the noise energy value of the smoothed noise signal in the first frequency band is a in the range of 100Hz to 200Hz, the noise energy value in the second frequency band is B in the range of 200Hz to 400Hz, and the noise energy value in the third frequency band is C in the range of 400Hz to 1000Hz, 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 ratios of the candidate sound energy values corresponding to the candidate frequency bands to the sound energy values of other frequency bands exceed a preset ratio.

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

step S371: a first ratio of the sound energy values of the candidate frequency bands to the sound energy values 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 first frequency band and a second frequency band, which are continuous, and it should be noted that the first frequency band and the second frequency band are continuous with the candidate frequency band in frequency, for example, 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 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 be continuous in frequency, for example, the candidate frequency band is 100Hz to 200Hz, the first frequency band may be 250Hz to 450Hz, and the second frequency band may be 500Hz to 1000 Hz.

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

As an embodiment, the first predetermined ratio is the same as the second predetermined ratio, and it may be 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 predetermined ratio or the second predetermined ratio, and if both the ratios exceed the first predetermined ratio or the second predetermined ratio, it is determined that the ratios of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of the other frequency bands all exceed the predetermined 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 a frequency difference between the first frequency band and the second frequency band, and in one example, the larger the frequency difference is, the larger the preset ratio may be. In another example, the larger the frequency difference is, the smaller the preset ratio may be, and the target noise reduction parameter may be determined only by sufficiently different sound energy values of the candidate frequency band and the frequency band closest to the candidate frequency band.

The frequency difference may be a difference between the center frequency of each of the first frequency band and the second frequency band and the candidate frequency band, or a difference between the upper limit frequency or the lower limit frequency of each of the first frequency band and the second frequency band and the candidate frequency band, which 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 150Hz is 150Hz, 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 150Hz is 550Hz, then 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 the preset ratio may be preset, and then the corresponding preset ratio may be determined according to the frequency difference to serve as the first preset ratio or the second preset ratio.

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

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

In some embodiments, if the ratios of the candidate sound energy value corresponding to the candidate frequency band and the sound energy values of the other frequency bands do not exceed the preset ratio, there may be a mixture of multiple noise signals, and at this time, the frequency band with the highest sound energy value may not be included in the frequency bands divided in advance, so that the sound energy values between the frequency bands are not different enough, at this time, the frequency band with the highest sound energy value may be widened, and the other frequency bands whose ratios do not exceed the preset ratio and the candidate frequency bands are merged into one target frequency band, so that the merged target frequency band may generate a corresponding noise reduction parameter as the target noise reduction parameter. Therefore, the method can realize targeted noise reduction processing on a scene mixed by multiple high-noise signals, and obtain a better noise reduction effect.

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

For example, if the sound energy value of the noise signal to be analyzed in the first frequency band of 100 Hz-200 Hz is A, the sound energy value in the second frequency band of 200 Hz-400 Hz is B, the sound energy value in the third frequency band of 400 Hz-1000 Hz is C, if A is larger than a certain multiple of B and A is larger than a certain multiple of C, that is, the first band 100Hz to 200Hz corresponding to A is a candidate band, the second band 200Hz to 400Hz is defined as the first band, the third band 400Hz to 1000Hz is defined as the second band, and the first ratio of A to B is greater than the first preset ratio, the second ratio of A to C is greater than the second preset ratio, the noise signal to be analyzed can be classified into a noise type, and the noise reduction parameter can be adjusted to be the noise reduction parameter with the best noise reduction performance aiming at the candidate frequency band of 100 Hz-200 Hz, so that deeper and more targeted noise reduction processing can be performed.

Step S390: and carrying out noise reduction processing on the environmental sound based on the target noise reduction parameters.

Based on the foregoing embodiment, the noise reduction processing method provided in this embodiment determines whether the ratios of the candidate sound energy value corresponding to the candidate frequency band to the sound energy values of the other frequency bands all exceed a preset ratio, and determines the noise reduction parameter corresponding to the candidate frequency band as the target noise reduction parameter only when the ratios all exceed the preset ratio, so as to determine whether the difference degree between the candidate sound energy value and the sound energy values of the other frequency bands is large enough, and then determines the noise reduction parameter corresponding to the candidate frequency band, that is, the noise reduction parameter whose noise reduction performance is mainly concentrated in the candidate frequency band, as the target noise reduction parameter only when the difference degree is large enough, thereby achieving more accurate noise reduction for various noise signals and improving the noise reduction effect.

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

the audio acquisition module 1010 is used for acquiring environmental sounds based on an audio acquisition device, wherein the environmental sounds comprise noise signals;

a preprocessing module 1020, configured to preprocess the acquired environmental sound to obtain a noise signal to be analyzed, where the noise signal to be analyzed corresponds to multiple frequency bands;

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

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

a denoising module 1050, configured to denoise the ambient sound based on the target denoising parameter.

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

a first candidate determining sub-module for determining a frequency band having a highest sound energy value among the plurality of frequency bands as a candidate frequency band;

the first candidate comparison submodule is used for determining whether the ratios of the candidate sound energy values corresponding to the candidate frequency bands and the sound energy values of other frequency bands exceed preset ratios or not;

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 candidate frequency bands exceed the target noise reduction parameters.

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

a target frequency band determining module, configured to combine, if none of the frequency bands exceeds the predetermined ratio, the other frequency bands whose ratios do not exceed the predetermined ratio with the candidate frequency band as target frequency bands;

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 which are consecutive, the preset ratio includes a first preset ratio and a second preset ratio, and the candidate comparison sub-module includes: first ratio determining unit, second ratio determining unit and ratio comparing unit, wherein:

a first ratio determination unit for determining a first ratio of the sound energy value of the candidate frequency band to the 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 ratios of the candidate sound energy values corresponding to the candidate frequency bands and the sound energy values of other frequency bands exceed a preset ratio if the first ratio exceeds a first preset ratio and the second ratio exceeds a second preset ratio.

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

a second candidate determining sub-module for determining a frequency band having a highest sound energy value among the plurality of frequency bands as a candidate frequency band if a ratio between the 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: a frequency band division submodule, a filter determination submodule and a time domain filtering submodule, wherein:

the frequency band division submodule is used for dividing a 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 submodule is used for carrying out band-pass filtering processing on the noise signal of each frequency band on the basis of the band-pass filter corresponding to each frequency band in the time domain to obtain a filtered noise signal serving as the noise signal to be analyzed.

Further, the preprocessing module 1020 may include: a down-sampling sub-module and a noise acquisition sub-module, wherein:

the down-sampling sub-module is used for performing down-sampling processing on the acquired environmental sound to obtain the down-sampled environmental sound;

and the noise acquisition submodule is used for obtaining a noise signal to be analyzed according to the down-sampled environmental sound.

Further, the preprocessing module 1020 may include: a model determination submodule and a parameter determination submodule, 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 determining 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 module, wherein:

and the smoothing processing module is used for smoothing the 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 reduction initiator sub-module, wherein:

and the noise reduction starting submodule is used for preprocessing the environment sound to obtain a noise signal to be analyzed if the sound energy value of the noise signal in the environment sound exceeds a preset energy value.

The noise reduction processing apparatus 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 are not described herein again.

In the several embodiments provided in the present application, the coupling between the modules may be electrical, mechanical or other type of coupling.

In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.

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 headset or a smartphone, a tablet computer, an MP3 player, an MP4 player, an electronic book, a notebook computer, a personal computer, a wearable electronic device, or the like, which is capable of running an application. 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 applications, wherein the one or more applications may be stored in the memory 1120 and configured to be executed by the one or more processors 1110, the one or more programs configured to perform a method as described in the aforementioned method embodiments.

Processor 1110 may include one or more processing cores. The processor 1110 interfaces with various components throughout the electronic device 1100 using various interfaces and circuitry to 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 hardware using at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1110 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is to be appreciated that the modem can be implemented by a single communication chip without being integrated into the processor 1110.

The Memory 1120 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 1120 may be used to store instructions, programs, code, sets of codes, or sets of instructions. 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 (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like. The stored data area may also store data created during use by the electronic device 1100 (e.g., phone books, audio-visual 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 capture device 1210, an audio output device 1220, and an audio signal processing circuit 1230. Wherein:

the audio acquisition device 1210 is used for acquiring environmental sounds. In some embodiments, the audio capture device 1210 can be a microphone or other device capable of capturing audio signals for capturing and transmitting to the audio signal processing circuit 1220.

The audio signal processing circuit 1220 is configured to obtain the environmental sound collected by the audio collecting 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 signal 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 plurality of frequency bands.

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

In addition, in some embodiments, the headset 1200 may further include a power supply circuit, which may supply power to other hardware components, and the power supply source may be a battery built in the headset 1200, may be a power input from the outside, or may be a power generating device built in the headset 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 are 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 called by a processor to execute the method 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 and programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 1300 includes a non-volatile computer-readable storage medium. The computer-readable storage medium 1300 has storage space for program code 1310 for performing any of the method steps described above. The program code can be read from or written to one or more computer program products. The program code 1310 may be compressed, for example, in a suitable form.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims

1. A method of noise reduction processing, 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 signal 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 multiple frequency bands;

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

2. The method according to claim 1, wherein determining the corresponding target noise reduction parameter according to the proportional relationship between the sound energy values of the plurality of frequency bands comprises:

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

determining whether the ratios of the candidate sound energy values corresponding to the candidate frequency bands to the sound energy values of other frequency bands exceed a preset ratio or not;

and if the number of the candidate frequency bands exceeds the preset threshold, determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters.

3. The method of claim 2, wherein after determining whether the ratios of the candidate sound energy values corresponding to the candidate frequency bands to the sound energy values of the other frequency bands exceed a preset ratio, the method further comprises:

if not, combining other frequency bands of which the ratios do not exceed the preset ratios with the candidate frequency bands to be used as target frequency bands;

and determining the noise reduction parameters corresponding to the target frequency band as target noise reduction parameters.

4. The method of claim 2, wherein the other frequency bands comprise a first frequency band and a second frequency band which are consecutive, the predetermined ratio comprises a first predetermined ratio and a second predetermined 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 predetermined ratio comprises:

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

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

5. The method according to claim 1, wherein determining the corresponding target noise reduction parameter according to the proportional relationship between the sound energy values of the plurality of frequency bands comprises:

if the proportion among the sound energy values of the plurality of frequency bands is matched with a preset proportion, determining the frequency band with the highest sound energy value from the plurality of frequency bands as a candidate frequency band;

and determining the noise reduction parameters corresponding to the candidate frequency bands as target noise reduction parameters.

6. The method according to any one of claims 1 to 5, wherein the pre-processing the collected environmental 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 performing band-pass filtering processing on the noise signal of each frequency band on the basis of the band-pass filter corresponding to each frequency band in a time domain to obtain a filtered noise signal serving as the noise signal to be analyzed.

7. The method of claim 1, wherein the pre-processing the collected environmental sound to obtain a noise signal to be analyzed comprises:

down-sampling the acquired environmental sound to obtain down-sampled environmental sound;

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

8. The method of claim 1, wherein after obtaining the sound energy values of the noise signal to be analyzed in a plurality of frequency bands, the method further comprises:

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

9. The method according to claim 1, wherein determining the corresponding target noise reduction parameter according to the proportional relationship between the sound energy values of the plurality of frequency bands comprises:

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

and determining the noise reduction parameters corresponding to the noise types as target noise reduction parameters.

10. The method of claim 1, wherein the pre-processing the environmental sound to obtain a noise signal to be analyzed comprises:

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

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

the audio acquisition module is used for acquiring environmental sounds acquired by the audio acquisition device, and the environmental sounds comprise noise signals;

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 multiple frequency bands;

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

12. An earphone, comprising an audio acquisition device, an audio output device and an 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 signal 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 multiple frequency bands;

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

13. 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 programs configured to perform the method of any of claims 1-10.

14. A computer-readable storage medium having program code stored therein, the program code being invoked by a processor to perform the method of any of claims 1-10.