CN111063368B - Method, apparatus, medium, and device for estimating noise in audio signal - Google Patents
Method, apparatus, medium, and device for estimating noise in audio signal Download PDFInfo
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- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
Abstract
The present invention relates to the field of data service technologies, and in particular, to a method, an apparatus, a medium, and a device for estimating noise in an audio signal. The inventors have found that the noise signal in the audio signal may include a blue-violet noise signal in the colored noise signal (the blue-violet noise signal may be understood as including a blue noise signal and a violet noise signal) in addition to the white noise signal and the pink noise signal in the colored noise signal. Therefore, the scheme provided by the embodiment of the present invention provides that when it is determined that the noise signal in the audio signal includes the colored noise signal, whether the colored noise signal includes the pink noise signal and the blue-violet noise signal may be continuously determined, and when it is determined that the colored noise signal includes the blue-violet noise signal, the noise estimation value may be determined by using the blue-violet noise estimation model with respect to the blue-violet noise signal. Therefore, the types of the noise signals can be further subdivided, and a more accurate noise estimation value is obtained through a corresponding noise estimation model.
Description
Technical Field
The present invention relates to the field of data service technologies, and in particular, to a method, an apparatus, a medium, and a device for estimating noise in an audio signal.
Background
In the scheme of suppressing the noise signal in the audio signal, the noise estimation value can be obtained by a noise estimation method, so that the noise signal suppression is performed. For example, as shown in fig. 1, an input signal (which may be a signal acquired by a microphone) may be divided into two paths, one path of the input signal is subjected to time-frequency transform to calculate spectral difference and successive signal-to-noise ratio, and the other path of the input signal is sampled to obtain discrete quantities and then calculate spectral flatness. The probability of the voice/noise can be calculated according to the calculated sequential signal-to-noise ratio, and the initial noise estimation in each frame can be updated according to the calculated voice/noise probability. And carrying out wiener filtering on the calculated initial noise estimation to obtain an estimated voice signal, carrying out frequency-time conversion, and carrying out signal synthesis with the calculated spectral flatness and spectral difference to obtain a final output frame, namely an output signal subjected to noise suppression. In the scheme shown in fig. 1, the successive signal-to-noise ratio needs to be calculated according to the noise estimation value determined by the noise estimation method.
Since the prior art considers that the noise signal in the audio signal mainly includes white noise and pink noise (pink noise can be understood as including red noise and pink noise), the current noise estimation method mainly aims at the white noise and the pink noise. As shown in fig. 2, it is first necessary to determine a white noise signal in the noise signal, determine a noise estimation value by using a white noise estimation model for the white noise signal, and determine a noise estimation value by using a pink noise estimation model for a non-white noise signal in the noise signal.
However, in real life, noise signals in the audio signals not only include white noise signals and pink noise signals, and therefore, the accuracy of the noise estimation value determined by the existing noise estimation method is low.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a medium and equipment for estimating noise in an audio signal, which are used for solving the problem of low accuracy of noise estimation in the audio signal.
A method of noise estimation in an audio signal, the method comprising:
determining whether a colored noise signal is included in a noise signal in the audio signal;
if the noise signals are determined to comprise colored noise signals, determining whether the colored noise signals comprise pink noise signals and blue-violet noise signals, wherein the blue-violet noise signals comprise blue noise signals and violet noise signals;
and if the colored noise signals are determined to comprise the blue-violet noise signals, determining a noise estimation value by using a blue-violet noise estimation model aiming at the blue-violet noise signals.
An apparatus for noise estimation in an audio signal, the apparatus comprising:
a first determination unit configured to determine whether a colored noise signal is included in a noise signal in the audio signal;
a second determining unit, configured to determine whether the colored noise signal includes a pink noise signal and a blue-violet noise signal if the first determining unit determines that the noise signal includes the colored noise signal, where the blue-violet noise signal includes the blue noise signal and the violet noise signal;
and a noise estimation unit, configured to determine, for the blue-violet noise signal, a noise estimation value by using a blue-violet noise estimation model if the second determination unit determines that the colored noise signal includes the blue-violet noise signal.
The present invention also provides a non-volatile computer storage medium having stored thereon an executable program for execution by a processor to perform the steps of implementing the method as described above.
The invention also provides a noise estimation device in the audio signal, which comprises a memory, a processor, a transceiver and a bus interface; the processor is used for reading the program in the memory and executing: receiving, by the transceiver, a noise signal in an audio signal and determining whether the noise signal includes a colored noise signal; if the noise signals are determined to comprise colored noise signals, determining whether the colored noise signals comprise pink noise signals and blue-violet noise signals, wherein the blue-violet noise signals comprise blue noise signals and violet noise signals; and if the colored noise signals are determined to comprise the blue-violet noise signals, determining a noise estimation value by using a blue-violet noise estimation model aiming at the blue-violet noise signals.
The inventors have found that the noise signal in the audio signal may include a blue-violet noise signal in the colored noise signal (the blue-violet noise signal may be understood as including a blue noise signal and a violet noise signal) in addition to the white noise signal and the pink noise signal in the colored noise signal. Therefore, the scheme provided by the embodiment of the present invention provides that when it is determined that the noise signal in the audio signal includes the colored noise signal, whether the colored noise signal includes the pink noise signal and the blue-violet noise signal may be continuously determined, and when it is determined that the colored noise signal includes the blue-violet noise signal, the noise estimation value may be determined by using the blue-violet noise estimation model with respect to the blue-violet noise signal. Therefore, the types of the noise signals can be further subdivided, and a more accurate noise estimation value is obtained through the corresponding noise estimation model.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of noise suppression of an audio signal provided by the prior art;
FIG. 2 is a schematic flow chart of a noise estimation method provided in the prior art;
FIG. 3 is a flowchart illustrating a method for estimating noise in an audio signal according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a noise estimation process of a blue-violet noise signal according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a noise estimation apparatus in an audio signal according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of a noise estimation apparatus in an audio signal according to a third embodiment of the present invention.
Detailed Description
The noise signals generated in the audio signals in daily life are not only white noise signals and pink noise signals, but also the existing noise estimation parameter fitting is not enough.
In consideration of the spectrum characteristic of the blue-violet noise signal, the embodiment of the invention provides that the blue-violet noise estimation model can adopt an f-noise model to obtain a more accurate noise estimation value of the blue-violet noise signal.
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. 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 invention.
It should be noted that, the "plurality" or "a plurality" mentioned herein means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The terms "first," "second," and the like in the description and claims of the present invention and in the preceding drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein.
Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
An embodiment of the present invention provides a method for estimating noise in an audio signal, where the flow of the steps of the method may be as shown in fig. 3, and the method includes:
step 101, determining whether a noise signal in an audio signal includes a colored noise signal.
In this step, the colored noise signal in the noise signal can be identified, so that the subsequent thinning processing can be continuously performed on the colored noise signal.
Of course, the noise estimation scheme provided by the present embodiment also relates to noise estimation of the white noise signal, and thus also relates to identification of the white noise signal. The identification of the white noise signal and the identification of the colored noise signal may be performed simultaneously or separately. The method for identifying a white noise signal in this embodiment is not limited, and may be implemented in any manner. The method for identifying the colored noise signal is not limited, and can be realized in any way. If it is determined that the noise signal includes a colored noise signal, the process may continue to step 102, and if it is determined that the noise signal includes a white noise signal, a noise estimation value may be determined by using a white noise estimation model for the white noise signal. In this step, it is exemplified that whether the noise signal in the audio signal includes the white noise signal and the colored noise signal is determined at the same time. Determining whether the noise signal in the audio signal includes a white noise signal and a colored noise signal may be achieved by any method, for example, but not limited to, by:
and determining whether the noise signals comprise noise signals with frequencies larger than a set value or not, if so, determining that the part of the noise signals are white noise signals, and determining the rest noise signals in the noise signals as colored noise signals.
That is, the white noise signal and the colored noise signal in the noise signal can be distinguished by the frequency of the noise signal according to the fact that the frequency of the colored noise signal (which can be understood to include a pinkish noise signal and a blue-violet noise signal) is low and the frequency of the white noise signal is high.
Step 102, determining whether the colored noise signals comprise pink noise signals and blue-violet noise signals.
If it is determined in step 101 that the noise signal includes a colored noise signal, in this step, it may be determined whether the colored noise signal includes a pink noise signal and a blue-violet noise signal.
Determining whether the colored noise signal includes a pink noise signal and a blue-violet noise signal may be implemented in any manner, for example, but not limited to, by:
determining whether the colored noise signals comprise noise signals with the frequency being the set red pink noise signal frequency, if yes, determining that the part of the noise signals are red pink noise signals, and determining the rest noise signals in the colored noise signals as blue-violet noise signals;
or, determining whether the colored noise signals include noise signals with the frequency of the set blue-violet noise signal, if so, determining that the part of the noise signals are blue-violet noise signals, and determining the rest noise signals in the colored noise signals as pink noise signals.
That is, since the colored noise signal of the non-white noise signal mainly includes the pink noise signal and the blue-violet noise signal, the pink noise signal can be identified, and the remaining noise signal in the colored noise signal can be determined as the blue-violet noise signal. Alternatively, the blue-violet noise signal can be identified and the remaining noise signal in the colored noise signal can be determined as the pink noise signal.
If it is determined that the noise signal includes a blue-violet noise signal, step 103 may be continued. If the noise signal is determined to include the pink noise signal, a noise estimation value can be determined by using a pink noise estimation model for the pink noise signal.
And 103, determining a noise estimation value by using a blue-violet noise estimation model.
If it is determined in step 102 that the noise signal includes a blue-violet noise signal, in this step, a blue-violet noise estimation model may be used to determine a noise estimation value for the blue-violet noise signal.
It should be noted that, in the prior art, according to the spectral characteristic of the white noise signal, the white noise estimation model can be determined by, but not limited to, the following ways:
the white noise estimation parameter is equal to the sum of the white noise spectrum/the length of the white noise spectrum, that is, the noise estimation parameter (which may be simply referred to as the white noise estimation parameter) for the white noise signal can be understood as the ratio of the sum of the white noise spectrum to the length of the white noise spectrum.
In the prior art, the pink noise estimation model is generally but not limited to a 1/f noise model according to the spectral characteristics of the pink noise signal.
1/f noise is a low frequency noise that is ubiquitous in nature. The 1/f noise model adopts a method of characterizing the power spectral density of a noise signal by a frequency index, and the power spectral density s (f) based on the 1/f noise model can be represented by the following formula (1):
S(f)＝a+b/f γ (l) (1)
wherein a is white noise amplitude, b is 1/f noise amplitude, and gamma is frequency index.
The frequency point of the spectrum curve of the actually measured noise signal is known as f (i), and the corresponding power spectral density value is known as S (i). According to equation (1), a power spectral density expression of the pilot band based on the 1/f noise model can be obtained as shown in equation (2):
S(i)＝b/f γ (i) (2)
wherein S (i) and f (i) are nonlinear relationships, so that logarithmic curve fitting can be adopted to convert the logarithmic curve fitting into the linear relationship, and a least square method is adopted to fit a curve model, so that a noise estimation value can be calculated according to the fitted curve model.
In the present invention, the blue-violet noise estimation model may, but is not limited to, adopt an f-noise model in consideration of the spectral characteristics of the blue-violet noise signal. f noise is a high-frequency noise that exists in nature.
In one implementation, determining the noise estimate value using the blue-violet noise estimation model may include, but is not limited to:
determining a power spectral density expression of the blue-violet noise signal represented by a frequency index using an f-noise model;
performing curve fitting according to the expression; and through curve fitting, selecting a proper curve type to fit the observation data, and performing curve fitting analysis on the curve relation because the frequency and the amplitude of the blue-violet noise signal are in the curve relation.
Determining a power spectrum of the blue-violet noise signal by a least square method; the sum of squares of residuals of the parameter estimation value and the observation value can be minimized by the least square method.
And determining the calculated minimum residual square of the power spectrum and the corresponding noise estimation value as the noise estimation value of the blue-violet noise signal. That is, the error between the estimated value and the observed value can be judged by the sum of the squares of the residuals, and a smaller sum of the squares of the residuals indicates a more accurate estimated value.
Specifically, a noise estimation flow diagram of the blue-violet noise signal may be as shown in fig. 4, and includes:
step 1: an f noise model is constructed for blue-violet noise, and the power spectral density of the blue-violet noise signal is expressed by a frequency index which can be expressed as
S(f)＝c+df β (l) (3)
c is the white noise amplitude, d is the f noise amplitude, and β is the frequency index.
The frequency point of the spectrum curve of the actually measured noise signal is known as f (i), and the corresponding power spectrum density value of the frequency point is known as S (i). According to the formula (3), a power spectral density expression of a frequency point in the main pilot band based on the f noise model can be obtained, which is as follows (4):
S(i)＝df β (i) (4)
wherein i is a positive integer not greater than m, and m is a positive integer not less than 2. S (i) and f (i) are unary non-linear relationships that cannot be fitted directly with the least squares method.
Step 2: and (6) fitting a curve. Converting the non-linear relation into the linear relation by mathematical transformation of the formula (4) in the step 1.
Taking logarithm on both sides of equal sign of formula (4) at the same time:
lg[S(i)]＝lgd+βlg[f(i)] (5)
order: it can be seen that the nonlinear relationship is converted into a linear relationship by y (i) ═ lg [ s (i) ], p ═ lgd, q ═ β, x (i) ═ lg [ f (i) ], and obtaining y (i) ═ p + qx (i).
And step 3: by least squares fitting, d (j) and β (j) can be obtained from the following equations.
Wherein j is a positive integer greater than 1 and less than m;
from the set of parameters d (j) and β (j), a fitted noise power spectral density of
S j (f)＝c(j)+d(j)f β(j) (8)
Wherein, the main guide frequency band of the f noise is set as [ f (i), f (j)]The dominant frequency band of white noise is [ f (j), f (m)]，1<j<m, one can obtain:
and 4, step 4: the residual sum of squares σ (j) is calculated by the following equation (9):
wherein, S (f) i ) Power spectral density expression, S, representing the main pilot band j (f i ) Representing the fitted noise power spectral density of the pilot band.
When j is 2 to m-1, the values of c (j), d (j), β (j), and σ (j) in the m-2 group can be obtained. Let j be the value of j corresponding to the minimum sigma (j) min Then c (j) min )、d(j min )、β(j min ) I.e. the best noise estimate sought.
In the existing technical scheme, only noise estimation values of two kinds of noise, namely white noise and pink noise, can be obtained. The scheme provided by the embodiment of the invention makes up the defects in the prior art, completes the noise types, further subdivides the noise types, and can determine more diversified noise types, thereby obtaining more accurate noise estimation values. And when subsequently carrying out noise suppression according to the noise estimation value, the noise in the audio signal can be more effectively suppressed, and a better auditory effect is brought.
Based on the same inventive concept as the first embodiment, the following apparatuses are provided.
Example two
An embodiment of the present invention provides an apparatus for estimating noise in an audio signal, where the apparatus may be configured as shown in fig. 5, and includes:
the first determination unit 11 is configured to determine whether a noise signal in the audio signal includes a colored noise signal; the second determining unit 12 is configured to determine whether the colored noise signal includes a pink noise signal and a blue-violet noise signal if the first determining unit determines that the noise signal includes the colored noise signal, where the blue-violet noise signal includes the blue noise signal and the violet noise signal; the noise estimation unit 13 is configured to determine, for the blue-violet noise signal, a noise estimation value by using a blue-violet noise estimation model if the second determination unit determines that the colored noise signal includes the blue-violet noise signal.
The first determining unit 11 is further configured to determine whether a white noise signal is included in the noise signal; the noise estimation unit 13 is further configured to determine a noise estimation value by using a white noise estimation model for the white noise signal if the first determination unit determines that the noise signal includes the white noise signal; and if the second determining unit determines that the colored noise signals comprise pink noise signals, determining a noise estimation value by using a pink noise estimation model for the pink noise signals.
The second determining unit 12 is configured to determine whether the colored noise signals include a pink noise signal and a blue-violet noise signal, and includes: determining whether the colored noise signals comprise noise signals with the frequency being the set red pink noise signal frequency, if yes, determining that the part of the noise signals are red pink noise signals, and determining the rest noise signals in the colored noise signals as blue-violet noise signals; or, determining whether the colored noise signals include noise signals with the frequency of the set blue-violet noise signal, if so, determining that the part of the noise signals are blue-violet noise signals, and determining the rest noise signals in the colored noise signals as pink noise signals.
The first determining unit 11 is configured to determine whether a noise signal in an audio signal includes a colored noise signal, and includes: and determining whether the noise signals comprise noise signals with frequencies larger than a set value or not, if so, determining that the part of the noise signals are white noise signals, and determining the rest noise signals in the noise signals as colored noise signals.
The blue-violet noise estimation model may, but is not limited to, employ an f-noise model.
The determining the noise estimation value by the noise estimation unit 13 using the blue-violet noise estimation model includes:
determining a power spectral density expression of the blue-violet noise signal represented by a frequency index using an f-noise model; performing curve fitting according to the expression; determining a power spectrum of the blue-violet noise signal by a least square method; and determining the calculated minimum residual square sum of the power spectrums and the corresponding noise estimation value as the noise estimation value of the blue-violet noise signal.
Based on the same inventive concept, embodiments of the present invention provide the following apparatus and medium.
EXAMPLE III
A third embodiment of the present invention provides a noise estimation device in an audio signal, where the structure of the device may be as shown in fig. 6, and the device includes a memory 21, a processor 22, a transceiver 23, and a bus interface; the processor 22 is configured to read the program in the memory 21, and execute: receiving, by the transceiver 23, a noise signal in an audio signal and determining whether a colored noise signal is included in the noise signal; if the noise signals are determined to comprise colored noise signals, determining whether the colored noise signals comprise pink noise signals and blue-violet noise signals, wherein the blue-violet noise signals comprise blue noise signals and violet noise signals; and if the colored noise signals are determined to comprise the blue-violet noise signals, determining a noise estimation value by using a blue-violet noise estimation model aiming at the blue-violet noise signals.
The processor 22 is further configured to determine whether the noise signal includes a white noise signal, and if it is determined that the noise signal includes the white noise signal, determine a noise estimation value by using a white noise estimation model for the white noise signal; and if the colored noise signals are determined to comprise pink noise signals, determining noise estimation values by using a pink noise estimation model aiming at the pink noise signals.
The processor 22 is configured to determine whether the colored noise signals include a pink noise signal and a blue-violet noise signal, and includes:
determining whether the colored noise signals comprise noise signals with the frequency being the set red pink noise signal frequency, if yes, determining that the part of the noise signals are red pink noise signals, and determining the rest noise signals in the colored noise signals as blue-violet noise signals;
or, determining whether the colored noise signals include noise signals with the frequency being the set blue-violet noise signal frequency, if yes, determining that the part of the noise signals are blue-violet noise signals, and determining the rest noise signals in the colored noise signals as pink noise signals.
The processor 22 is configured to determine whether a noise signal in the audio signal includes a colored noise signal, and includes:
and determining whether the noise signals comprise noise signals with frequencies larger than a set value or not, if so, determining that the part of the noise signals are white noise signals, and determining the rest noise signals in the noise signals as colored noise signals.
The blue-violet noise estimation model may employ an f-noise model.
The processor 22 determining the noise estimate value using the blue-violet noise estimation model comprises:
determining a power spectral density expression of the blue-violet noise signal represented by a frequency index using an f-noise model;
performing curve fitting according to the expression;
determining a power spectrum of the blue-violet noise signal by a least square method;
and determining the calculated minimum residual square of the power spectrum and the corresponding noise estimation value as the noise estimation value of the blue-violet noise signal.
Optionally, the processor 22 may specifically include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), one or more integrated circuits for controlling program execution, a hardware circuit developed by using a Field Programmable Gate Array (FPGA), or a baseband processor.
Optionally, the processor 22 may include at least one processing core.
Alternatively, the memory 21 may include a Read Only Memory (ROM), a Random Access Memory (RAM), and a disk memory. The memory 21 is used for storing data required by the at least one processor 22 during operation. The number of the memory 21 may be one or more.
A fourth embodiment of the present invention provides a non-volatile computer storage medium, where the computer storage medium stores an executable program, and when the executable program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
In particular implementations, computer storage media may include: a Universal Serial Bus flash drive (USB), a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other storage media capable of storing program codes.
In the embodiments of the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the described unit or division of units is only one division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical or other form.
The functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be an independent physical module.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device, such as a personal computer, a server, or a network device, or a processor (processor) to execute all or part of the steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media that can store program codes, such as a universal serial bus flash drive (usb flash drive), a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (13)
1. A method of noise estimation in an audio signal, the method comprising:
determining whether a colored noise signal is included in a noise signal in the audio signal;
if the noise signals are determined to comprise colored noise signals, determining whether the colored noise signals comprise pink noise signals and blue-violet noise signals, wherein the blue-violet noise signals comprise blue noise signals and violet noise signals;
if the colored noise signals are determined to comprise blue-violet noise signals, determining a noise estimation value by using a blue-violet noise estimation model aiming at the blue-violet noise signals;
wherein the determining a noise estimation value using a blue-violet noise estimation model comprises:
determining a power spectral density expression of the blue-violet noise signal represented by a frequency index by using a blue-violet noise estimation model; performing curve fitting according to the power spectral density expression; fitting the observed data by curve fitting; and determining the power spectral density of the blue-violet noise signal by a least square method, judging the error between the noise estimation value and the observation data based on the residual square sum of the power spectral density, and determining the minimum residual square sum of the power spectral density as the noise estimation value of the blue-violet noise signal.
2. The method of claim 1, wherein the method further comprises:
determining whether the noise signal comprises a white noise signal, and if the noise signal comprises the white noise signal, determining a noise estimation value by using a white noise estimation model aiming at the white noise signal; and if the colored noise signals are determined to comprise pink noise signals, determining noise estimation values by using a pink noise estimation model aiming at the pink noise signals.
3. The method of claim 1 or 2, wherein the determining whether the colored noise signal includes a pink noise signal and a blue-violet noise signal comprises:
determining whether the colored noise signals comprise noise signals with the frequency being the set red pink noise signal frequency, if yes, determining that the part of the noise signals are red pink noise signals, and determining the rest noise signals in the colored noise signals as blue-violet noise signals;
or, determining whether the colored noise signals include noise signals with the frequency of the set blue-violet noise signal, if so, determining that the part of the noise signals are blue-violet noise signals, and determining the rest noise signals in the colored noise signals as pink noise signals.
4. The method of claim 1 or 2, wherein determining whether a colored noise signal is included in the noise signal in the audio signal comprises:
and determining whether the noise signals comprise noise signals with frequencies larger than a set value or not, if so, determining that the part of the noise signals are white noise signals, and determining the rest noise signals in the noise signals as colored noise signals.
5. An apparatus for estimating noise in an audio signal, the apparatus comprising:
a first determination unit configured to determine whether a colored noise signal is included in a noise signal in the audio signal;
a second determining unit, configured to determine whether the colored noise signal includes a pink noise signal and a blue-violet noise signal if the first determining unit determines that the noise signal includes the colored noise signal, where the blue-violet noise signal includes the blue noise signal and the violet noise signal;
a noise estimation unit, configured to determine, for the blue-violet noise signal, a noise estimation value by using a blue-violet noise estimation model if the second determination unit determines that the colored noise signal includes the blue-violet noise signal;
wherein the determining a noise estimation value using a blue-violet noise estimation model comprises:
determining a power spectral density expression of the blue-violet noise signal represented by a frequency index by using a blue-violet noise estimation model; performing curve fitting according to the power spectral density expression; fitting the observed data by curve fitting; and determining the power spectral density of the blue-violet noise signal by a least square method, judging the error between the noise estimation value and the observation data based on the residual square sum of the power spectral density, and determining the minimum residual square sum of the power spectral density as the noise estimation value of the blue-violet noise signal.
6. The apparatus of claim 5, wherein the first determining unit is further configured to determine whether a white noise signal is included in the noise signal;
the noise estimation unit is further configured to determine a noise estimation value by using a white noise estimation model for the white noise signal if the first determination unit determines that the noise signal includes the white noise signal; and if the second determining unit determines that the colored noise signals comprise pink noise signals, determining a noise estimation value by using a pink noise estimation model for the pink noise signals.
7. The apparatus according to claim 5 or 6, wherein the second determining unit is configured to determine whether the colored noise signal includes a pink noise signal and a blue-violet noise signal, and includes:
determining whether the colored noise signals comprise noise signals with the frequency being the set red pink noise signal frequency, if yes, determining that the part of the noise signals are red pink noise signals, and determining the rest noise signals in the colored noise signals as blue-violet noise signals;
or, determining whether the colored noise signals include noise signals with the frequency of the set blue-violet noise signal, if so, determining that the part of the noise signals are blue-violet noise signals, and determining the rest noise signals in the colored noise signals as pink noise signals.
8. The apparatus as claimed in claim 5 or 6, wherein the first determining unit for determining whether a colored noise signal is included in the noise signal in the audio signal comprises:
and determining whether the noise signals comprise noise signals with frequencies larger than a set value or not, if so, determining that the part of the noise signals are white noise signals, and determining the rest noise signals in the noise signals as colored noise signals.
9. A non-transitory computer storage medium storing an executable program for execution by a processor to perform the steps of the method of any one of claims 1 to 4.
10. An apparatus for estimating noise in an audio signal, comprising a memory, a processor, a transceiver, and a bus interface; the processor is used for reading the program in the memory and executing: receiving, by the transceiver, a noise signal in an audio signal and determining whether the noise signal includes a colored noise signal; if the noise signals are determined to comprise colored noise signals, determining whether the colored noise signals comprise pink noise signals and blue-violet noise signals, wherein the blue-violet noise signals comprise blue noise signals and violet noise signals; if the colored noise signals are determined to comprise blue-violet noise signals, determining a noise estimation value by using a blue-violet noise estimation model aiming at the blue-violet noise signals;
wherein the determining a noise estimate value using a blue-violet noise estimation model comprises:
determining a power spectral density expression of the blue-violet noise signal represented by a frequency index by using a blue-violet noise estimation model; performing curve fitting according to the power spectral density expression; fitting the observed data by curve fitting; and determining the power spectral density of the blue-violet noise signal by a least square method, judging the error between the noise estimation value and the observation data based on the residual square sum of the power spectral density, and determining the minimum residual square sum of the power spectral density as the noise estimation value of the blue-violet noise signal.
11. The apparatus of claim 10, wherein the processor is further configured to determine whether the noise signal comprises a white noise signal, and if the noise signal comprises the white noise signal, determine a noise estimation value using a white noise estimation model for the white noise signal; and if the colored noise signals are determined to comprise pink noise signals, determining noise estimation values by using a pink noise estimation model aiming at the pink noise signals.
12. The apparatus of claim 10 or 11, wherein the processor to determine whether the colored noise signal includes a pink noise signal and a blue-violet noise signal comprises:
determining whether the colored noise signals comprise noise signals with the frequency being the set red pink noise signal frequency, if yes, determining that the part of the noise signals are red pink noise signals, and determining the rest noise signals in the colored noise signals as blue-violet noise signals;
or, determining whether the colored noise signals include noise signals with the frequency of the set blue-violet noise signal, if so, determining that the part of the noise signals are blue-violet noise signals, and determining the rest noise signals in the colored noise signals as pink noise signals.
13. The apparatus of claim 10 or 11, wherein the processor, for determining whether colored noise signals are included in noise signals in the audio signal, comprises:
and determining whether the noise signals comprise noise signals with frequencies larger than a set value or not, if so, determining that the part of the noise signals are white noise signals, and determining the rest noise signals in the noise signals as colored noise signals.
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