CN112163117A - Noise detection method and device and electronic equipment - Google Patents
Noise detection method and device and electronic equipment Download PDFInfo
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Abstract
The application discloses a noise detection method, a noise detection device and electronic equipment, and belongs to the technical field of communication. The problem that the accuracy of detection and processing of the piano noise by the electronic equipment is low can be solved. The method comprises the following steps: under the condition of playing a multimedia file, acquiring a first parameter of target single-frame data, wherein the target single-frame data is any frame data to be played in the multimedia file, and the first parameter comprises at least one of the following items: a first band energy and a first loudness; and detecting whether the target single-frame data has noise or not according to target parameters, wherein the target parameters at least comprise a first parameter. The embodiment of the application is applied to the detection and processing process of the piano noise by the electronic equipment.
Description
Technical Field
The application belongs to the technical field of communication, and particularly relates to a noise detection method and device and electronic equipment.
Background
Generally, when a user plays a multimedia file (e.g., an audio file) through an electronic device, and the audio file contains a piano tone, the tone pitch of the initial tone of the piano tone may coincide with the natural frequency of the speaker, thereby causing distortion of the speaker, so that there may be noise in the piano tone played by the electronic device. At present, the electronic equipment can determine the piano tones in the audio files by detecting the timbre of audio stream data of the audio files in real time, so that when the piano tones are detected, the piano tones can be subjected to balanced suppression (namely, the piano tones are subjected to large-amplitude suppression in a loudspeaker resonance frequency domain) to debug partial parameters of loudspeakers through a piano tone control algorithm, and thus, the control of the noise is realized.
However, in the above method, when the electronic device detects the piano tone, the electronic device performs the equalization suppression processing on the piano tone, and when there is no noise in the piano tone, the electronic device still performs the equalization suppression processing, which affects the timbre and volume of the piano tone, so that the accuracy of the electronic device in detecting and processing the piano noise is low.
Disclosure of Invention
The embodiment of the application aims to provide a noise detection method, a noise detection device and electronic equipment, and the problem that the accuracy of detection and processing of piano noise by the electronic equipment is low can be solved.
In order to solve the technical problem, the present application is implemented as follows:
in a first aspect, an embodiment of the present application provides a noise detection method, where the method includes: under the condition of playing a multimedia file, acquiring a first parameter of target single-frame data, wherein the target single-frame data is any frame data to be played in the multimedia file, and the first parameter comprises at least one of the following items: a first band energy and a first loudness; and detecting whether the target single-frame data has noise or not according to target parameters, wherein the target parameters at least comprise the first parameters.
In a second aspect, an embodiment of the present application provides a noise detection apparatus, including: the device comprises an acquisition module and a detection module. The acquiring module is configured to acquire a first parameter of target single-frame data when the multimedia file is played, where the target single-frame data is any frame data to be played in the multimedia file, and the first parameter includes at least one of the following: a first band energy and a first loudness. And the detection module is used for detecting whether the target single-frame data has noise or not according to the target parameters, wherein the target parameters at least comprise a first parameter.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instructions stored on the memory and executable on the processor, and when executed by the processor, the program or instructions implement the steps of the method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a program or instructions are stored, which when executed by a processor implement the steps of the method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or instructions to implement the method according to the first aspect.
In this embodiment of the application, when a multimedia file is played, the electronic device may obtain first frequency band energy and/or first loudness of target single frame data to be played in the multimedia file, so as to detect whether the target single frame data has noise according to the first frequency band energy and/or the first loudness. When the electronic equipment plays the multimedia file, the electronic equipment can detect whether the target single-frame data has noise in advance according to the first parameter by acquiring the first parameter of the target single-frame data to be played in the multimedia file, so that whether the target single-frame data needs to be processed or not can be determined in advance. Therefore, in the embodiment of the application, whether each single frame of data has a noise or not can be respectively determined by acquiring the single frame of data in the multimedia file in advance, so that when a certain single frame of data has a noise, the single frame of data can be processed without influencing the tone and the volume of the data, and the accuracy of detecting and processing the piano noise by the electronic equipment is high.
Drawings
FIG. 1 is a schematic diagram of a piano tone time domain signature and control target provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of the piano frequency domain characteristics and control targets provided by the embodiment of the application;
fig. 3 is a schematic diagram of a noise detection method according to an embodiment of the present application;
fig. 4 is a second schematic diagram of a noise detection method according to an embodiment of the present application;
fig. 5 is a third schematic diagram of a noise detection method according to an embodiment of the present application;
FIG. 6 is a fourth schematic diagram of a noise detection method according to an embodiment of the present application;
FIG. 7 is a fifth schematic diagram illustrating a noise detection method according to an embodiment of the present application;
FIG. 8 is a sixth schematic view illustrating a noise detection method according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a noise detection apparatus according to an embodiment of the present application;
fig. 10 is a second schematic structural diagram of a noise detection apparatus according to an embodiment of the present application;
fig. 11 is a third schematic structural diagram of a noise detection apparatus according to an embodiment of the present application;
fig. 12 is a fourth schematic structural diagram of a noise detection apparatus according to an embodiment of the present application;
fig. 13 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present disclosure;
fig. 14 is a second hardware structure schematic diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
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, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced in sequences other than those illustrated or described herein, and that the terms "first," "second," and the like are generally used herein in a generic sense and do not limit the number of terms, e.g., the first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.
The following explains some concepts and/or terms involved in the noise detection method provided by the embodiment of the present invention.
Generally, the perfection of a loudspeaker is inversely proportional to its physical size, given a reasonable design; the micro and small speakers carried by the electronic equipment face a series of distortion problems, which are limited in that the acoustic design cannot be completely solved, and only can be balanced and adjusted according to the subjective hearing of people. The noise generated by the piano tone is one of the bad subjective audiences caused by the distortion of many speakers. It is presently believed that the signal characteristic that excites such a noise is primarily a sharp onset timbre, and when the pitch of the onset timbre coincides with the loudspeaker natural frequency, the distortion will be further exacerbated, forming a persistent, distinct noise. The control method of the traditional device tuning to piano noise is to carry out balanced suppression: namely, the resonance frequency of the loudspeaker is greatly suppressed. The consequence of this suppression is a reduction in amplitude and volume for the full audio signal and a certain negative impact on the timbre. The piano sound processing module is superior to equalization suppression in judgment of signal characteristic detection, the signal characteristic detection is mainly used for detecting whether the current audio stream data characteristic has piano sound risks, and some schemes have some predictability, so that most of audios without piano sound risks are not controlled, and volume and timbre are restored. In the aspect of suppression control, besides performing balanced suppression, some other schemes compensate the piano noise control algorithm to a certain extent so as to maintain the original loudness. The piano sound control algorithm realizes the control of the minimum cost of the noise by referring to the parameters of the loudspeaker part and subjectively debugging the characteristic sound source.
The noise detection method provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.
In the embodiment of the application, when the electronic device plays the multimedia file, the electronic device may obtain first frequency band energy (or first loudness) of any frame data to be played in the multimedia file, so that the electronic device may detect whether the any frame data has noise according to the first frequency band energy. Specifically, the electronic device may perform a first filtering process on the arbitrary frame data to obtain a first frequency band energy of the arbitrary frame data, determine that the arbitrary frame data is piano sound data when the first frequency band energy satisfies a first preset condition, perform a second filtering process on the arbitrary frame data to obtain a second frequency band energy (or a second loudness), so as to determine that the piano sound data (i.e., the arbitrary frame data) has a noise risk when the second frequency band energy satisfies a second preset condition, then determine a noise risk type (i.e., a transition-band piano noise type or a delayed-period piano noise type) to which the piano sound data belongs according to a difference between the second frequency band energy and a frequency band energy of the historical single frame data, so as to obtain parameters (a vibration onset time, a preset tone vibration onset and a release time) corresponding to the noise risk type to which the piano sound data belongs, and calculating according to the parameters to obtain a target gain value, and performing compensation processing on initial frequency band energy (or initial loudness) corresponding to a frequency within a preset risk frequency band in at least one frequency corresponding to the target single frame data according to the target gain value, so that the accuracy of detecting and processing the piano noise by the electronic equipment can be improved.
In the embodiment of the application, the detection and control of the piano tones and quasi-piano tones are based on the basic time domain and frequency domain characteristics. The classic piano timbre time-domain envelope diagram is shown in fig. 1, wherein 101 is marked with a start-up time, in the timbre start-up stage, energy begins to suddenly rise compared with historical audio stream data, and the whole curve reaches a marked point 102 through sound start and attenuation. The time point 101 to 102 is used as an unstable transition zone (corresponding to the noise of a transition band piano), and is often about 15 to 20 milliseconds, which is an important factor for exciting the noise of the piano. The piano tone envelope then goes to a relatively steady sustain interval (corresponding to the delayed piano murmur) and ends at the mark point 103. The energy content of the sustain interval is another factor of piano tone excitation, and this time interval is typically between 500 and 2000 milliseconds for normal piano tones. The sustain interval is also the main time interval in which the piano tones are subjectively felt. The timbre then completes a complete musical tone process over an interval of release time. From a time domain perspective, particular attention to the onset 101 is required to suppress a single piano tone, and the suppression takes effect from this point, since the audio stream is processed online, with no future trend information, resulting in some suppression control of the transition band energy if the later delay energy or loudness is insufficient. The magnitude of the pressing control is related to the tuning parameters, and according to the magnitude set by the pressing parameters, the energy of the transition zone and the delay period can be simultaneously controlled, so that the final output reaches the target curve form marked by 104. Where care is taken to adjust the release time of the effect so that it can match the maximum duration of a certain type of piano tone.
In the embodiment of the present application, as shown in fig. 2, a common piano timbre frequency domain characteristic diagram is shown. The piano tone comprising a fundamental frequency f as a tone color0And several harmonics f1～fn. The distribution decreases as the number of harmonics increases, seen in the overall spectral energy. The common 88-key piano has obvious high-frequency missing phenomenon in keys with three groups or less of small characters. This is also one of the most obvious features of piano tones. The absence of high frequencies causes the high frequency shadowing phenomenon to disappear completely, resulting in minimal distortion being perceived significantly. Another concern with piano tone distortion is the energy of the risk band, which is labeled as an example in FIG. 2 at the fundamental tone frequency f0Upper, fourth harmonic f3The following is a description. The occurrence position of the risk band is related to the low-frequency resonance frequency of the loudspeaker. The objective of this patent algorithm suppression is also to control the harmonic (or fundamental) component in this interval so that it is attenuated by a certain proportion. The point marked 201 in fig. 2 shows a frequency point that needs to be controlled, and 202 shows a target frequency point after being controlled. At the same time, the critical frequency band corresponds toConsidering the corresponding lift can serve two purposes: compensating for volume loss and masking critical band noise that may remain. Fig. 2 shows an example of a boost frequency point of an adjacent frequency band 203. Through the identification and control of the time domain and frequency domain characteristics, the tone with high noise risk is changed and output from the two aspects of time domain and frequency domain, thereby realizing the control of the piano noise.
An embodiment of the present application provides a noise detection method, and fig. 3 shows a flowchart of the noise detection method provided in the embodiment of the present application, where the method may be applied to an electronic device. As shown in fig. 3, the noise detection method provided in the embodiment of the present application may include steps 201 and 202 described below.
In an embodiment of the present application, the target single frame data is any frame data to be played in a multimedia file, and the first parameter includes at least one of: a first band energy and a first loudness.
Optionally, in this embodiment of the application, the electronic device may determine whether there is a noise in the target single frame data by a piano sound suppression algorithm based on physical quantities such as amplitude and energy by acquiring frequency band energy of the target single frame data.
Optionally, in this embodiment of the present application, after acquiring the target single frame data, the electronic device may perform high-pass or high-frequency band-pass filtering on the target single frame data according to the piano tone high-frequency missing phenomenon to obtain a first criterion of the murmur (i.e., a timbre criterion).
Optionally, in this embodiment of the application, the electronic device may perform simulation and control based on the psychoacoustic perception quantity, so as to achieve an implementation manner of piano tone detection and suppression.
Optionally, in this embodiment of the present application, the electronic device may select a characteristic band-pass filter bank (critical band-pass filter banks) to filter the target single frame data. The physiological topological structure of the inner ear of a human is reflected by a critical frequency band, and the structure leads to the perception of certain bandwidth above and below a certain fixed central frequency pointHas limited auditory frequency domain resolution (leading to masking, pops, etc.). Wherein the effective bandwidth (ERB) and the center frequency f of the critical bandcThe relationship of (a) to (b) is as follows:
ERB(fc)＝24.7×(4.37×fc+1)Hz
the critical band excitation data is used as the basic decision data for the timbre determination and control in the next step.
Optionally, in the embodiment of the present application, the electronic device may evaluate the loudness of the adjacent frequency band of the critical frequency band in which the high-risk frequency band (i.e., the resonance frequency band of the speaker of the electronic device) is located, and if the loudness in the adjacent frequency band does not exceed the hearing threshold, there is no reverse masking effect and there is a risk of piano noise; if the loudness in adjacent bands exceeds the hearing threshold, there is a reverse masking effect and the piano murmur risk is low or zero.
In an embodiment of the present application, the target parameter at least includes a first parameter.
Optionally, in this embodiment of the present application, the electronic device determines whether there is a noise in the target single frame data by determining the first frequency band energy or the first loudness, where the noise may be a piano noise.
Optionally, in this embodiment of the application, the electronic device calculates loudness on the premise of determining the front and back masking and the frequency domain masking, sets a threshold in each critical frequency band according to a weighting curve such as an equal loudness curve, and when the loudness of the target single frame data exceeds the threshold, the target single frame data has a risk of piano noise, and when the loudness of the target single frame data does not exceed the threshold, the target single frame data does not have a risk of piano noise (i.e., low risk or zero risk). Wherein, the mutual occlusion model may adopt a triangle simplified model, that is, the loudness P of the target single frame data is sequentially occluded by the loudness P' of other single frame data, and the final loudness occlusion intensity Mlt of the target single frame data is determined by the loudness of other single frame data and the frequency at which the target single frame data is located:
ml(P,P’)＝yl(P’)-km|H(P’)-H(P)|
Mlt＝20log10(∑10ml(P，P’)/20)|p≠p’；Mlt≥0
where ml is the monophonic masking effect of the loudness P 'of the other single frame data on the loudness P of the target single frame data, and is determined by the supra-auditory threshold value yl and the adjustable gradient constant k of the loudness P' of the other single frame datam(typically set to 25 dB/critical band) and a single-frequency pure tone energy value H (P fundamental).
It is an object of the present invention to provide a method for detecting and controlling a single piano tone, which can improve the accuracy of piano tone detection and suppression, unlike conventional algorithms based on tone detection and control. This application combines the pitch frequency distribution of piano sound through the time domain characteristic mode of judging, and the fundamental frequency that the piano sound appears is accurately judged. And the power value of the historical single-frame data and the current single-frame data (namely the target single-frame data) are combined to judge the piano noise. Judging the piano tone oscillation starting time point through the time domain characteristics, and carrying out more accurate processing on the piano tone through the obtained piano noise base frequency and oscillation starting time so as to complete the control on the piano noise. Compared with the piano sound control algorithm in the prior art, the algorithm is improved in the aspect of operation amount, but the algorithm runs well in the integrated environment of several power amplifier chips at present. The control strategy is based on the control of single frame data with the risk of piano noise instead of the control strategy of all data, so that the control of non-piano noise in non-piano tones or piano tones is not ensured, and the true degree of the sound source reproduction is improved.
The embodiment of the application provides a noise detection method, and electronic equipment can acquire first frequency band energy and/or first loudness of target single frame data to be played in a multimedia file when the multimedia file is played, so as to detect whether the target single frame data has noise or not according to the first frequency band energy and/or the first loudness. When the electronic equipment plays the multimedia file, the electronic equipment can detect whether the target single-frame data has noise in advance according to the first parameter by acquiring the first parameter of the target single-frame data to be played in the multimedia file, so that whether the target single-frame data needs to be processed or not can be determined in advance. Therefore, in the embodiment of the application, whether each single frame of data has a noise or not can be respectively determined by acquiring the single frame of data in the multimedia file in advance, so that when a certain single frame of data has a noise, the single frame of data can be processed without influencing the tone and the volume of the data, and the accuracy of detecting and processing the piano noise by the electronic equipment is high.
Optionally, in this embodiment of the application, the target parameter includes a first parameter. Referring to fig. 3, as shown in fig. 4, the step 201 may be specifically realized by a step 201a described below, and the step 202 may be specifically realized by a step 202a described below.
In this embodiment of the present application, the first parameter is a parameter of the target single frame data after the first filtering processing.
Optionally, in this embodiment of the present application, the electronic device may perform high-pass or high-frequency band-pass filtering on the target single frame data to obtain a timbre criterion of the murmur, and determine a frequency band energy (i.e., a first frequency band energy) of the target single frame data.
Optionally, in this embodiment of the present application, the electronic device may process the target single frame data to determine the loudness (i.e., the first loudness) of the target single frame data.
Optionally, in this embodiment of the application, the first preset condition may be any one of the following: the energy of the first frequency band is less than or equal to a first preset energy, the first loudness is greater than or equal to a first preset loudness, and the like.
Optionally, in this embodiment of the application, when the first parameter is a first frequency band energy, and the first frequency band energy is greater than a first preset energy, the electronic device determines that the target single frame data is non-piano sound data, and there is no risk of piano murmur.
Optionally, in this embodiment of the application, when the first parameter is the first loudness, and the first loudness is smaller than the first preset loudness, the electronic device determines that the target single-frame data is non-piano sound data and there is no risk of piano noise.
It should be noted that the electronic device performs the murmur risk assessment on the target single-frame data according to the tone criteria of the piano tones. When the energy of the characteristic frequency band exceeds a certain preset energy, the target single frame data is judged to be non-piano tone.
In the embodiment of the application, the electronic device can perform first filtering processing on the target single-frame data to acquire the first parameter, so that when the first parameter does not satisfy the first preset condition, the target single-frame data is determined to be non-piano sound data and no piano noise risk exists, and whether the piano noise risk exists in the single-frame data can be accurately determined.
Optionally, in this embodiment of the application, the target parameter includes a first parameter and a second parameter. With reference to fig. 3, as shown in fig. 5, the step 202 may be specifically implemented by the following step 202b, and after the step 202b, the noise detection method provided in the embodiment of the present application may further include the following step 301.
In an embodiment of the present application, the second parameter is a parameter of target single frame data after the second filtering process, and the second parameter includes at least one of the following: a second band energy and a second loudness.
Optionally, in this embodiment of the application, when the first parameter is a first frequency band energy, and the first frequency band energy is less than or equal to a first preset energy, the electronic device determines that the target single frame data is piano sound data.
Optionally, in this embodiment of the application, when the first parameter is the first loudness, and the first loudness is greater than or equal to the first preset loudness, the electronic device determines that the target single frame data is piano sound data.
Optionally, in this embodiment of the application, after the electronic device determines that the target single frame data is piano sound data, the electronic device may perform a second filtering process on the target single frame data to obtain a second frequency band energy or a second loudness.
Optionally, in this embodiment of the application, the second parameter includes a second frequency band energy. The step 202b can be realized by the step 202b1 and the step 202b2 described below.
Step 202b1, if the first parameter meets a first preset condition, the electronic device determines that the target single-frame data is piano tone data, determines a target frequency in a preset risk frequency band in at least one frequency corresponding to the target single-frame data, and acquires frequency band energy corresponding to the target frequency.
Alternatively, in the embodiment of the present application, the electronic device analyzes the key frequency domain distribution specificity of the piano tones, and the piano tones on the keys define a specific set of center frequencies according to ISO 16, wherein with 440Hz as the reference frequency, the twelve-tone law can deduce the frequency f (N) of N semitones at a distance of a440 tones:
f(n)＝440×2n/12
in this step, the specific frequency point is subjected to narrow-band filtering, and the electronic device may obtain the RMS (i.e. the energy of the second frequency band) of the single-frame average energy or the frequency of the frequency falling into the risk band in fig. 2.
Step 202b2, the electronic device determines the second frequency band energy according to the frequency band energy corresponding to the target frequency.
And step 301, the electronic equipment determines whether the piano sound data has noise risks according to the second parameters.
Optionally, in this embodiment of the application, the electronic device may determine whether the energy of the second frequency band exceeds a second preset energy, so as to determine whether the piano sound data has a noise risk.
Optionally, in this embodiment of the application, the electronic device may consider one by one whether the absolute energy of the frequency band in the filter bank exceeds a second preset energy, and if the absolute energy of the frequency band in the filter bank does not exceed the second preset energy, the target single frame data is determined to be free of piano noise risk (i.e., low-risk piano noise or zero-risk piano noise); if the absolute energy of the frequency bands in the filter bank exceeds a second preset energy, the target single frame data is determined to be at risk of piano noise.
Optionally, in this embodiment of the application, the selection of the second preset energy is related to a parameter of a speaker of the electronic device and a data size of the multimedia file.
In the embodiment of the application, the electronic device may determine whether the first parameter meets a first preset condition, so that when the first parameter meets the first preset condition, the target single-frame data is determined to be piano sound data, the target single-frame data is subjected to second filtering processing, so that second frequency band energy and/or second loudness is obtained, and whether the target single-frame data has a noise risk is determined according to the second frequency band energy and/or the second loudness, so that the electronic device may accurately determine whether the single-frame data has a piano noise risk.
Optionally, in this embodiment of the application, with reference to fig. 5, as shown in fig. 6, the step 301 may be specifically implemented by the following step 301a or step 301b, and before the step 301a and the step 301b, the noise detection method provided in this embodiment of the application further includes a step 401.
Optionally, in this embodiment of the application, the second preset condition may be any one of the following: the energy of the second frequency band is greater than or equal to a second preset energy, the second loudness is greater than or equal to a second preset loudness, and the like.
Optionally, in this embodiment of the application, when the second parameter is a second frequency band energy, and the second frequency band energy is smaller than a second preset energy, the electronic device determines that there is no noise risk in the piano sound data.
Optionally, in this embodiment of the application, when the second parameter is the second loudness, and the second loudness is smaller than the second preset loudness, the electronic device determines that there is no noise risk in the piano sound data.
And step 301b, if the second parameter meets a second preset condition, the electronic equipment determines that the piano sound data has noise risk, and determines the noise risk type of the piano sound data according to the target difference.
In this embodiment of the application, the target difference is a difference between the second parameter and a third parameter of the historical single-frame data, where the third parameter is a parameter of the historical single-frame data after the second filtering processing.
Optionally, in this embodiment of the application, when the second parameter is a second frequency band energy, and the second frequency band energy is greater than or equal to a second preset energy, the electronic device determines that the piano tone data has a noise risk.
Optionally, in this embodiment of the application, when the second parameter is the second loudness, and the second loudness is greater than or equal to the second preset loudness, the electronic device determines that the piano sound data has a noise risk.
Optionally, in this embodiment of the application, after the electronic device determines that the piano sound data has a noise risk, the electronic device may acquire a third parameter of the historical single-frame data after the second filtering processing, and calculate a target difference between the second parameter and the third parameter.
Alternatively, in the embodiment of the present application, the electronic device may determine the type of the noise risk (i.e., the transition-band piano noise type or the delayed-period piano noise type) to which the piano sound data belongs, according to the target difference between the second parameter and the third parameter.
Optionally, in this embodiment of the application, the electronic device may determine an increment of the loudness of the target single frame data, and if the increment of the loudness exceeds a third preset loudness of a certain frequency band in the filter bank, the electronic device determines that the target single frame data is of a transition band musical instrument noise type.
Optionally, in this embodiment of the present application, the third preset loudness is set in relation to a loudness variation detection threshold JND.
Optionally, in this embodiment of the application, with reference to fig. 6, as shown in fig. 7, the step 301b may be specifically implemented by the following step 301b1 or step 301b2, and before the step 301b1 and the step 301b2, the noise detection method provided in this embodiment of the application further includes a step 402.
Step 301b1, if the second parameter meets a second preset condition, the electronic device determines that the piano sound data has a noise risk, and if the target difference is greater than or equal to a first preset threshold, determines that the noise risk type to which the piano sound data belongs is a transition zone piano noise type.
Step 301b2, if the second parameter meets a second preset condition, the electronic device determines that the piano sound data has a noise risk, and if the target difference is smaller than a first preset threshold, determines that the noise risk type to which the piano sound data belongs is a delayed piano noise type.
It should be noted that the electronic device may further analyze the target single frame data with the noise risk to determine whether the piano noise risk in the target single frame data is a transition-band piano noise type or a delayed-period piano noise type, specifically, the electronic device compares the second parameter obtained in step 202b with a parameter of the historical single frame data corresponding to the target single frame data, if the difference does not exceed a first preset threshold, it is determined that the target single frame data is the delayed-period piano noise type, and if the difference exceeds the first preset threshold, it is determined that the target single frame data is the transition-band piano noise type. Transition band piano noise type processing needs to be synchronized with piano tone attack, and therefore further analysis of attack time is required.
In the embodiment of the application, the electronic device can judge whether the second parameter meets the second preset condition, so that when the second parameter does not meet the second preset condition, the piano sound data is determined to have no noise risk; or when the second parameter meets a second preset condition, determining that the piano sound data has a noise risk, and determining a noise risk type of the piano sound data according to a difference value between the second parameter and a third parameter of the historical single-frame data subjected to the second filtering processing, so that the electronic equipment can accurately determine the noise risk type of the piano sound data.
Optionally, in this embodiment of the present application, as shown in fig. 8 with reference to fig. 6, after "determining the noise risk type to which the piano sound data belongs" in step 301b, the noise detection method provided in this embodiment of the present application may further include steps 501 and 502 described below.
And step 501, the electronic equipment acquires a fourth parameter corresponding to the noise risk type to which the piano sound data belongs.
In an embodiment of the present application, the fourth parameter includes: the starting time, the preset timbre starting sound and the releasing time.
Optionally, in this embodiment of the application, the electronic device may perform oscillation starting time point analysis on the target single frame data to determine a fourth parameter of the target single frame data.
And 502, calculating by the electronic equipment according to the fourth parameter to obtain a target gain value, and performing compensation processing on a fifth parameter corresponding to the target frequency according to the target gain value.
In an embodiment of the application, the target frequency is a frequency in a preset risk frequency band in at least one frequency corresponding to the target single frame data, and the fifth parameter includes at least one of: initial band energy and initial loudness of the target single frame data.
Optionally, in this embodiment of the present application, the electronic device may further include: and (3) processing the target single-frame data according to any risk type of a piano-free noise risk type (namely low risk or zero risk), a transition ribbon piano noise type and a delayed piano noise type. And calculating a target gain value according to the fourth parameter so as to load the target gain value into the risk frequency band, thereby suppressing or reducing the risk of the noise of the piano tones.
Optionally, in this embodiment of the application, after the electronic device performs the compensation process on the fifth parameter corresponding to the target frequency according to the target gain value, the electronic device may perform energy compensation on the pressed frequency band (i.e., the frequency band adjacent to the risk frequency band) to compensate the volume so as to further control the weak type noise.
Optionally, in this embodiment of the application, after the electronic device performs compensation processing on the fifth parameter corresponding to the target frequency according to the target gain value, the electronic device may ensure that the output loudness of the target single-frame data is the same as the loudness of the original signal by calculating a loudness model of the target single-frame data.
In the embodiment of the application, the electronic equipment can accurately detect the single-frame data of piano tones in the multimedia file with the risk of piano murmur under the condition that the operation amount allows, so that the realization of parallel multi-tone time domain narrow-band filtering becomes possible. The frequency characteristics of the piano temperament system are fully utilized. Continuous and zero delay (no buffering and pre-reading required) of piano tone compression is achieved. The method is beneficial to judging the noise type of the transition band piano and the noise type of the delayed piano, judging the mode and processing the dynamic pressing curve. Small noise masking is achieved by compensating the piano tone volume (the compensation strategy allows small noise to exist, which will likely be masked due to the enhancement of the critical upper and lower frequency bands). And by accurately reconstructing harmonic components of the piano, the tone color and harmonic distribution of the piano tone are easier to control due to the accurate positioning of the pitch with piano murmur.
In the embodiment of the application, the electronic device further uses a psychoacoustic model to enable the embodiment of the application to achieve the above effects, and further, according to human perception, the loudness threshold is more accurate for judging the tone oscillation through the critical frequency band excitation level loudness. The critical band filter bank can be used to directly judge the effect of mutual masking between frequency bands. According to the effect of tone mutual masking, more direct piano tone risk judgment can be carried out, namely when the risk frequency band is strongly masked (including front-back time masking and frequency domain masking), the piano noise is simultaneously masked, and the risk of generating piano noise is greatly reduced. Therefore, the loudness can be directly compensated through calculation, in addition, a tone frequency domain template can be constructed through a table look-up model, and a balance point for restoring tone and volume is determined in modification control.
In the embodiment of the application, after the electronic device determines the noise risk type to which the piano sound data belongs, the electronic device may obtain the start-up time, the preset timbre start-up sound and the release time corresponding to the noise risk type to which the piano sound data belongs, so as to obtain a target gain value through calculation according to the parameters, and perform compensation processing on the initial frequency band energy or the initial loudness corresponding to the frequency within the preset risk frequency band in at least one frequency corresponding to the target single frame data according to the target gain value, so that the accuracy of detection and processing of the piano noise by the electronic device is high.
It should be noted that, in the noise detection method provided in the embodiment of the present application, the execution main body may be a noise detection device, or a control module in the noise detection device for executing the noise detection method. In the embodiment of the present application, a method for performing loading noise detection by a noise detection apparatus is taken as an example, and the noise detection apparatus provided in the embodiment of the present application is described.
Fig. 9 shows a schematic diagram of a possible structure of the noise detection device according to the embodiment of the present application. As shown in fig. 9, the noise detection device 70 may include: an acquisition module 71 and a detection module 72.
The obtaining module 71 is configured to obtain a first parameter of target single frame data when the multimedia file is played, where the target single frame data is any frame data to be played in the multimedia file, and the first parameter includes at least one of: a first band energy and a first loudness. The detecting module 72 is configured to detect whether there is a noise in the target single frame data according to a target parameter, where the target parameter at least includes a first parameter.
In one possible implementation, the target parameter includes a first parameter. Referring to fig. 9, as shown in fig. 10, the noise detection apparatus 70 according to the embodiment of the present application may further include: a processing module 73. The processing module 73 is configured to perform a first filtering process on the target single-frame data to obtain a first parameter, where the first parameter is a parameter of the target single-frame data after the first filtering process. The detecting module 72 is specifically configured to determine that the target single frame data is non-piano sound data and no noise exists if the first parameter does not satisfy the first preset condition.
In one possible implementation, the target parameter includes a first parameter and a second parameter. The detecting module 72 is specifically configured to determine that the target single frame data is piano sound data if the first parameter meets a first preset condition. With reference to fig. 9 and as shown in fig. 11, the noise detection apparatus 70 according to the embodiment of the present application may further include: a processing module 73 and a determination module 74. The processing module 73 is configured to perform second filtering processing on the target single-frame data to obtain a second parameter, where the second parameter is a parameter of the target single-frame data after the second filtering processing, and the second parameter includes at least one of the following parameters: a second band energy and a second loudness. A determination module 74 for determining whether the piano tone data has a noise risk according to the second parameter.
In a possible implementation manner, the determining module 74 is specifically configured to determine that the piano sound data has no noise risk if the second parameter does not satisfy the second preset condition; or if the second parameter meets a second preset condition, determining that the piano sound data has a noise risk, and determining a noise risk type of the piano sound data according to a target difference value, wherein the target difference value is a difference value between the second parameter and a third parameter of the historical single-frame data, and the third parameter is a parameter of the historical single-frame data after the second filtering processing.
In a possible implementation manner, the determining module 74 is specifically configured to determine that the type of the noise risk to which the piano sound data belongs is a transition zone piano noise type if the target difference is greater than or equal to a first preset threshold; or if the target difference is smaller than a first preset threshold, determining that the noise risk type to which the piano sound data belongs is the delayed piano noise type.
In a possible implementation manner, the obtaining module 71 is further configured to obtain a fourth parameter corresponding to the noise risk type to which the piano sound data belongs after determining the noise risk type to which the piano sound data belongs according to the target difference, where the fourth parameter includes: the starting time, the preset timbre starting sound and the releasing time. Referring to fig. 11, as shown in fig. 12, the noise detection apparatus 70 according to the embodiment of the present application may further include: a calculation module 75. The calculating module 75 is configured to calculate a target gain value according to the fourth parameter obtained by the obtaining module 71. The processing module 73 is further configured to perform compensation processing on a fifth parameter corresponding to a target frequency according to the target gain value, where the target frequency is a frequency in a preset risk frequency band in at least one frequency corresponding to the target single frame data, and the fifth parameter includes at least one of the following: initial band energy and initial loudness of the target single frame data.
In one possible implementation, the second parameter includes a second band energy. The processing module 73 is specifically configured to determine a target frequency within a preset risk frequency band in at least one frequency corresponding to the target single-frame data, and acquire frequency band energy corresponding to the target frequency; and determining the energy of the second frequency band according to the energy of the frequency band corresponding to the target frequency.
The noise detection device provided in the embodiment of the present application can implement each process implemented by the noise detection device in the above method embodiments, and for avoiding repetition, detailed description is not repeated here.
The embodiment of the application provides a noise detection device, and when an electronic device plays a multimedia file, the electronic device can detect whether noise exists in target single-frame data in advance according to a first parameter by acquiring the first parameter of the target single-frame data to be played in the multimedia file, so that whether the target single-frame data needs to be processed or not can be determined in advance. Therefore, in the embodiment of the application, whether each single frame of data has a noise or not can be respectively determined by acquiring the single frame of data in the multimedia file in advance, so that when a certain single frame of data has a noise, the single frame of data can be processed without influencing the tone and the volume of the data, and the accuracy of detecting and processing the piano noise by the electronic equipment is high.
The noise detection device in the embodiment of the present application may be a device, or may be a component, an integrated circuit, or a chip in a terminal. The device can be mobile electronic equipment or non-mobile electronic equipment. By way of example, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine or a self-service machine, and the like, and the embodiments of the present application are not particularly limited.
The noise detection device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, and embodiments of the present application are not limited specifically.
Optionally, as shown in fig. 13, an electronic device M00 is further provided in this embodiment of the present application, and includes a processor M01, a memory M02, and a program or an instruction stored in the memory M02 and executable on the processor M01, where the program or the instruction when executed by the processor M01 implements the processes of the foregoing noise detection method embodiment, and can achieve the same technical effects, and details are not repeated here to avoid repetition.
It should be noted that the electronic device in the embodiment of the present application includes the mobile electronic device and the non-mobile electronic device described above.
Fig. 14 is a schematic hardware structure diagram of an electronic device implementing an embodiment of the present application.
The electronic device 100 includes, but is not limited to: a radio frequency unit 101, a network module 102, an audio output unit 103, an input unit 104, a sensor 105, a display unit 106, a user input unit 107, an interface unit 108, a memory 109, and a processor 110.
Those skilled in the art will appreciate that the electronic device 100 may further comprise a power source (e.g., a battery) for supplying power to various components, and the power source may be logically connected to the processor 110 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The electronic device structure shown in fig. 14 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than those shown, or combine some components, or arrange different components, and thus, the description is not repeated here.
The processor 110 is configured to, in a case that a multimedia file is played, obtain a first parameter of target single frame data, where the target single frame data is any frame data to be played in the multimedia file, and the first parameter includes at least one of: a first band energy and a first loudness; and detecting whether the target single-frame data has noise or not according to the target parameters, wherein the target parameters at least comprise a first parameter.
The embodiment of the application provides an electronic device, and when the electronic device plays a multimedia file, the electronic device can detect whether the target single frame data has noise in advance according to a first parameter by acquiring the first parameter of the target single frame data to be played in the multimedia file, so that whether the target single frame data needs to be processed or not can be determined in advance. Therefore, in the embodiment of the application, whether each single frame of data has a noise or not can be respectively determined by acquiring the single frame of data in the multimedia file in advance, so that when a certain single frame of data has a noise, the single frame of data can be processed without influencing the tone and the volume of the data, and the accuracy of detecting and processing the piano noise by the electronic equipment is high.
It should be understood that, in the embodiment of the present application, the input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, and the Graphics Processing Unit 1041 processes image data of a still picture or a video obtained by an image capturing device (such as a camera) in a video capturing mode or an image capturing mode. The display unit 106 may include a display panel 1061, and the display panel 1061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 107 includes a touch panel 1071 and other input devices 1072. The touch panel 1071 is also referred to as a touch screen. The touch panel 1071 may include two parts of a touch detection device and a touch controller. Other input devices 1072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein. The memory 109 may be used to store software programs as well as various data including, but not limited to, application programs and an operating system. The processor 110 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the noise detection method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and so on.
The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement each process of the noise detection method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as system-on-chip, system-on-chip or system-on-chip, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method of noise detection, the method comprising:
under the condition of playing a multimedia file, acquiring a first parameter of target single-frame data, wherein the target single-frame data is any frame data to be played in the multimedia file, and the first parameter comprises at least one of the following items: a first band energy and a first loudness;
and detecting whether the target single-frame data has noise or not according to target parameters, wherein the target parameters at least comprise the first parameters.
2. The method of claim 1, wherein the target parameter comprises the first parameter;
the acquiring of the first parameter of the target single-frame data includes:
performing first filtering processing on the target single-frame data to obtain the first parameter, wherein the first parameter is a parameter of the target single-frame data after the first filtering processing;
the detecting whether the target single-frame data has noise according to the target parameters includes:
and if the first parameter does not meet a first preset condition, determining that the target single-frame data is non-piano sound data and no noise exists.
3. The method of claim 1, wherein the target parameters include the first parameter and a second parameter;
the detecting whether the target single-frame data has noise according to the target parameters includes:
if the first parameter meets a first preset condition, determining that the target single-frame data is piano sound data, and performing second filtering processing on the target single-frame data to obtain a second parameter, where the second parameter is a parameter of the target single-frame data after the second filtering processing, and the second parameter includes at least one of the following: a second band energy and a second loudness;
and determining whether the piano sound data has noise risk according to the second parameter.
4. The method according to claim 3, wherein said determining whether the piano tone data is at risk of a murmur based on the second parameter comprises:
if the second parameter does not meet a second preset condition, determining that the piano sound data has no noise risk;
and if the second parameter meets a second preset condition, determining that the piano sound data has a noise risk, and determining a noise risk type of the piano sound data according to a target difference value, wherein the target difference value is a difference value between the second parameter and a third parameter of the historical single-frame data, and the third parameter is the parameter of the historical single-frame data after the second filtering processing.
5. The method according to claim 4, wherein said determining a type of noise risk to which the piano sound data belongs based on the target difference comprises:
if the target difference is larger than or equal to a first preset threshold value, determining that the noise risk type to which the piano sound data belongs is a transition band piano noise type;
and if the target difference is smaller than a first preset threshold value, determining that the noise risk type to which the piano sound data belongs is a delayed piano noise type.
6. The method according to claim 5, wherein after determining the type of the noise risk to which the piano sound data belongs based on the target difference, the method further comprises:
acquiring a fourth parameter corresponding to the noise risk type to which the piano sound data belongs, wherein the fourth parameter comprises: the method comprises the steps of starting vibration time, and presetting timbre starting vibration sound and releasing sound time;
calculating a target gain value according to the fourth parameter, and performing compensation processing on a fifth parameter corresponding to a target frequency according to the target gain value, where the target frequency is a frequency in a preset risk frequency band in at least one frequency corresponding to the target single frame data, and the fifth parameter includes at least one of the following: initial band energy and initial loudness of the target single frame of data.
7. A noise detection device, comprising: the device comprises an acquisition module and a detection module;
the acquiring module is configured to acquire a first parameter of target single frame data when a multimedia file is played, where the target single frame data is any frame data to be played in the multimedia file, and the first parameter includes at least one of the following: a first band energy and a first loudness;
the detection module is configured to detect whether there is a noise in the target single frame data according to a target parameter, where the target parameter at least includes the first parameter.
8. The noise detection device according to claim 7, wherein the target parameter includes the first parameter;
the noise detection device further includes: a processing module;
the processing module is configured to perform first filtering processing on the target single-frame data to obtain the first parameter, where the first parameter is a parameter of the target single-frame data after the first filtering processing;
the detection module is specifically configured to determine that the target single frame data is non-piano sound data and no noise exists if the first parameter does not satisfy a first preset condition.
9. The noise detection device according to claim 7, wherein the target parameter includes the first parameter and a second parameter;
the detection module is specifically configured to determine that the target single-frame data is piano sound data if the first parameter meets a first preset condition;
the noise detection device further includes: a processing module and a determining module;
the processing module is configured to perform second filtering processing on the target single-frame data to obtain the second parameter, where the second parameter is a parameter of the target single-frame data after the second filtering processing, and the second parameter includes at least one of the following parameters: a second band energy and a second loudness;
and the determining module is used for determining whether the piano sound data has noise risks according to the second parameters.
10. The noise detection device according to claim 9, wherein the determination module is specifically configured to determine that the piano sound data has no noise risk if the second parameter does not satisfy a second preset condition; or if the second parameter meets a second preset condition, determining that the piano sound data has a noise risk, and determining a noise risk type of the piano sound data according to a target difference, wherein the target difference is a difference between the second parameter and a third parameter of the historical single-frame data, and the third parameter is a parameter of the historical single-frame data after the second filtering processing.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105336344A (en) * | 2014-07-10 | 2016-02-17 | 华为技术有限公司 | Noise detection method and apparatus thereof |
| CN108322868A (en) * | 2018-01-19 | 2018-07-24 | 瑞声科技（南京）有限公司 | Improve the method that loud speaker plays piano voice sound quality |
| US20190074805A1 (en) * | 2017-09-07 | 2019-03-07 | Cirrus Logic International Semiconductor Ltd. | Transient Detection for Speaker Distortion Reduction |
| CN111343540A (en) * | 2020-03-05 | 2020-06-26 | 维沃移动通信有限公司 | Piano audio processing method and electronic equipment |
-
2020
- 2020-09-18 CN CN202010984664.1A patent/CN112163117A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105336344A (en) * | 2014-07-10 | 2016-02-17 | 华为技术有限公司 | Noise detection method and apparatus thereof |
| US20190074805A1 (en) * | 2017-09-07 | 2019-03-07 | Cirrus Logic International Semiconductor Ltd. | Transient Detection for Speaker Distortion Reduction |
| CN108322868A (en) * | 2018-01-19 | 2018-07-24 | 瑞声科技（南京）有限公司 | Improve the method that loud speaker plays piano voice sound quality |
| CN111343540A (en) * | 2020-03-05 | 2020-06-26 | 维沃移动通信有限公司 | Piano audio processing method and electronic equipment |
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