CN106128469A - A kind of multiresolution acoustic signal processing method and device - Google Patents
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Abstract
The invention discloses a kind of multiresolution acoustic signal processing method and device, described method, including: sub-signal carries out discrete Fourier transform, obtains first order sequence；Zero point is inserted in described first order sequence；First order sequence after described insertion zero point is carried out inverse discrete Fourier transform change；First order sequence after described inverse discrete fourier transform is decomposed, obtains the eigen mode of multiple second level and write number；Number of writing the eigen mode of each second level carries out discrete Fourier transform (DFT), obtains the eigen mode of the second level and writes several sequences；Removing the eigen mode of the second level to write the number coefficient in zero point insert division, the second level eigen mode obtaining shortening is write several sequences；Several sequence of writing each second level eigen mode shortened carries out inverse discrete fourier transform, obtains the eigen mode of the final second level and writes number.The multiresolution acoustic signal processing method of the present invention and device, it is possible to reduce and decompose number of times and energy loss.
Description
Technical Field
The present invention relates to the field of image information processing technologies, and in particular, to a method and an apparatus for processing a multi-resolution audio signal.
Background
With the continuous development of information technology, shooting technology and shooting technology can capture details of objects in a very detailed manner, the content types of image videos are increasing explosively, and the spectrum information of the image videos is expanding, so that the requirements of people on the transmission quality and the service of the image videos are higher and higher. Linear non-adaptive information hierarchical extraction techniques, such as wavelet techniques and tree filter banks, have gradually failed to meet the requirements of people for information detail information extraction. In the picture video target detection, the feature detail information has an important influence on the classification accuracy. Therefore, there is a need for an information analysis and extraction method with more layers, more effective information extraction, and less information damage.
In the general technology, global effective information of a signal can be extracted from some components with larger bandwidth, such as general outlines of human backgrounds in picture videos, shapes of organs in medical pictures, and local detail information can continuously decompose component signals with larger bandwidth into narrow-band signals for extraction, which directly affects the quality of the signals, such as the definition of the picture videos, and is a key step for signal denoising.
The information extraction technology from the global level to the detail level and the layer-by-layer iteration is widely applied to the engineering field, such as the scalable image coding, the edge extraction of the image and the like, and the layered multi-resolution signal analysis technology provides a pyramid frame type signal analysis mode and provides a powerful tool for the signal processing analysis technology.
The most common layered multi-resolution signal analysis techniques in the prior art are wavelet analysis and tree-structured filter banks. Taking audio signal separation as an example: in the signal decomposition process, the first layer and the second layer of the decomposition occupy most of the energy of the signal, but the bandwidth ratio of the sub-signal is wide in each layer, and the background sound is still mixed in the decomposed signal, because the wavelet analysis and the tree filter bank are linear and non-adaptive, and the energy of the signal mainly falls into a narrow frequency band. Therefore, a multi-layer decomposition is required to be performed on the signal, but the wavelet analysis and the tree filter bank are not ideal, and there is a certain trade-off relationship between the decomposition level and the energy, namely: the more layers are decomposed, the narrower the bandwidth of the subband signal is, but the energy loss increases. Moreover, in wavelet analysis and the design of tree-structured filter banks, the computational operations are linear and the presettibility of the wavelet kernel results in an inability to perform efficient processing analysis on some non-linear adaptive signals.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for processing a multi-resolution audio signal, so as to solve the problems of multiple layers and large damage to information in the process of processing an image signal in the prior art.
The invention solves the problems through the following technical means:
in one aspect, the present invention provides a multi-resolution audio signal processing method, including:
performing discrete Fourier transform on the sub-signals to obtain a first-stage DFT sequence;
inserting a zero point into the first-stage DFT sequence to obtain a first-stage sequence after the zero point is inserted;
performing inverse discrete Fourier transform on the first-stage sequence after the zero point is inserted to obtain a first-stage sequence after inverse discrete Fourier transform;
decomposing the first-stage sequence after the inverse discrete Fourier transform to obtain a plurality of second-stage eigenmode functions;
performing discrete Fourier transform on the intrinsic mode function of each second level to obtain a DFT sequence of the intrinsic mode function of the second level;
removing the coefficient of the second-level eigenmode function at the zero insertion position of the DFT sequence of each second-level eigenmode function to obtain a shortened DFT sequence of the second-level eigenmode function;
and performing inverse discrete Fourier transform on the DFT sequence of each shortened second-stage eigenmode function to obtain a final second-stage eigenmode function.
Furthermore, the number of elements in the first-stage sequence after zero insertion is conjugate and symmetric.
Further, before performing discrete fourier transform on the sub-signals, the method further includes: the original signal is decomposed into a plurality of sub-signals.
In another aspect, the present invention provides a multi-resolution audio signal processing apparatus, including:
the first transformation module is used for carrying out discrete Fourier transformation on the decomposed sub-signals;
the inserting module is used for inserting zero points into the sub-signals after the discrete Fourier transform;
the first inverse transformation module is used for carrying out inverse discrete Fourier transformation on the sub-signals inserted with the zero points;
the first decomposition module is used for carrying out second-stage decomposition on the sub-signals subjected to the inverse discrete Fourier transform;
the second transformation module is used for carrying out discrete Fourier transformation on the subsignals subjected to the second-stage decomposition;
the removing module is used for removing the coefficient of the second-level sub-signal after the discrete Fourier transform at the zero point insertion position to obtain a shortened second-level sub-signal;
and the second inverse transformation module is used for carrying out inverse discrete Fourier transformation on the shortened second-stage sub-signals.
Furthermore, the inserting module inserts zero into the sequence of the sub-signals after the discrete fourier transform, and the number of elements in the sequence after the zero is inserted is conjugate and symmetric.
Further, the multi-resolution audio signal processing apparatus further includes:
and the second decomposition module is used for decomposing the superior signal.
Compared with the prior art, the multi-resolution audio signal processing method and the device have the following beneficial effects:
1. the method can be used for carrying out self-adaptive signal processing analysis aiming at nonlinearity, and has wider application range.
2. The wavelet kernel does not need to be set in advance, the problem of wavelet optimal basis search does not exist, and the method has a stable analysis effect on unsteady signals.
3. For the effect achieved by the wavelets of the same grade, the method has less decomposition times and less energy loss.
Drawings
The invention is further described below with reference to the figures and examples.
Fig. 1 is a flowchart of a multi-resolution audio signal processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a multi-resolution audio signal processing method according to an embodiment of the present invention;
FIG. 3 is a graph of the distribution of an input audio signal for a mixed bird sound and a stream sound used in an embodiment of the present invention;
fig. 4 is a time domain analysis diagram after a decomposition process is performed on the input audio signal of fig. 3 by the multi-resolution audio signal processing method of the present invention;
fig. 5 is a diagram of energy distribution of each frequency band after primary decomposition processing of the input audio signal of fig. 3 by the multi-resolution audio signal processing method of the present invention;
fig. 6 is a time domain analysis diagram after performing a secondary decomposition process on the input audio signal of fig. 3 by the multi-resolution audio signal processing method of the present invention;
fig. 7 is a diagram of energy distribution of each frequency band after performing a secondary decomposition process on the input audio signal of fig. 3 by the multi-resolution audio signal processing method of the present invention;
FIG. 8 is a diagram of time domain analysis after processing the input audio signal of FIG. 3 by prior art wavelet analysis;
FIG. 9 is a graph of energy distribution for each frequency band after processing the input audio signal of FIG. 3 by prior art wavelet analysis;
fig. 10 is a schematic structural diagram of a multi-resolution audio signal processing apparatus according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
Example one
The present invention will be described in detail below with reference to fig. 1 and 2, and the present embodiment provides a multi-resolution audio signal processing method, as shown in fig. 1 and 2, including:
101. the original signal is decomposed into a plurality of sub-signals.
102. Eigenmode function of sub-signalPerforming Discrete Fourier Transform (DFT) to obtain DFT sequence of the first stage
Wherein the eigenmode Function is called Intrinsic Mode Function (IMF) for short,where n is a coefficient index of the upper IMF in the frequency domain, and ζ is the ζ -th upper IMF, which is an IMF used for the re-decomposition.Where n is the coefficient index of the superior IMF in the frequency domain, and ζ is the ζ -th superior IMF.
103. Inserting zeros in DFT sequencesObtaining the first-stage sequence after inserting the zero point
Wherein,the number of elements after zero insertion is conjugate and symmetrical, and is irrelevant to the number of zero insertion and the position of zero insertion.And k' IS the coefficient index of the second-stage zero-inserted IMF in the frequency domain, IS means the inserted sequence, and IS the zero-inserted DFT sequence.
104. For the sequences after insertion of zero pointPerforming Inverse Discrete Fourier Transform (IDFT) to obtain first-stage sequence after inverse discrete Fourier transform
Wherein,the IS refers to an inserted sequence, which IS a DFT sequence after zero insertion, and n' IS a signal length of a discrete signal after inverse discrete Fourier transformDegree of rotation
105. For the first-stage sequence after inverse discrete Fourier transformDecomposing to obtain the second level of eigenmode functionWherein m is1＝0,1，2…M1-1。M1As the total number of second decomposition
The Decomposition performed in this embodiment refers to an Empirical Mode Decomposition (EMD) method, which performs signal Decomposition according to the time scale features of the data itself without setting any basis function in advance. This is essentially different from the fourier decomposition and wavelet decomposition methods that are built on a priori harmonic basis functions and wavelet basis functions. Due to the characteristics, the EMD method can be theoretically applied to the decomposition of any type of signals, so that the EMD method has obvious advantages in processing non-stationary and non-linear data, is suitable for analyzing non-linear and non-stationary signal sequences and has high signal-to-noise ratio. Therefore, the EMD method has been proposed to be rapidly and effectively applied to different engineering fields, such as ocean, atmosphere and celestial observation and seismic record analysis, mechanical fault diagnosis, damping identification of dense-frequency power systems and modal parameter identification of large civil engineering structures. The key of the method is empirical Mode decomposition, which can decompose a complex signal into a finite number of eigenmode functions (IMFs for short), and each decomposed IMF component contains local characteristic signals of different time scales of an original signal. The empirical mode decomposition method can enable non-stationary data to be subjected to stationary processing, and then Hilbert transform is carried out to obtain a time-frequency spectrogram, so that frequency with physical significance is obtained. This method is intuitive, straightforward, a posteriori, and adaptive compared to short-time fourier transforms, wavelet decompositions, etc., because the basis functions are decomposed from the data itself. The decomposition is adaptive because it is based on the local characteristics of the time scale of the signal sequence.
Wherein,m1 is the total number of the lower stage IMFs after the re-solution, and n' is the signal length of the lower stage IMF (zero point is not removed).
106. For each second level of eigenmode functionsPerforming Discrete Fourier Transform (DFT) to obtain a second-stage eigenmode function of a DFT sequenceWherein m is1＝0,1，2…M1-1。M1As the total number of second decomposition
Wherein,m1 is the total number of the lower IMFs after the re-decomposition, and k' is the coefficient index of the lower IMF after the zero insertion in the frequency domain.
107. For each DFT sequence of the second level eigenmode functions, removeCoefficient at zero insertion to obtain a shortened second stage eigenmode functionThe DFT sequence of (1).
Wherein,m1 is the total number of the lower IMFs after the re-decomposition, and k is the coefficient index of the IMF in the frequency domain after the zero point is removed.
108. For each shortened DFT sequencePerforming IDFT to obtain final second stage eigenmode function
Wherein,where m1 denotes the total number of lower-stage IMFs after the re-decomposition, and n denotes the coefficient index of the IMF after the inverse fourier transform in the frequency domain.
It should be noted that, in this embodiment, the original signal is processed only once, and the original signal processed by the above scheme may be processed multiple times according to the requirement on the fineness of signal processing.
Taking the input audio signal of fig. 3 in which bird sounds and water sounds are mixed as an example, if the background water sounds are regarded as noise, the noise of the signal is very noticeable.
Fig. 4 and 5 are a time domain analysis diagram and an energy distribution diagram of each frequency band, respectively, after performing a primary decomposition process on the input audio signal of fig. 3 by the multi-resolution audio signal processing method of the present invention.
Fig. 6 and 7 are a time domain analysis diagram and an energy distribution diagram of each frequency band, respectively, after performing a secondary decomposition process on the input audio signal of fig. 3 by the multi-resolution audio signal processing method of the present invention.
Fig. 8 and 9 are a time domain analysis diagram and an energy distribution diagram of each band, respectively, after processing by wavelet analysis.
Obviously, the multi-resolution audio signal processing method provided by the invention has the advantages that the noise is obviously reduced after the input audio signal is processed for one time or more times, and the energy loss is small. The denoising effect after the existing wavelet analysis processing is very poor, and the energy loss is very serious.
The invention provides a new nonlinear self-adaptive signal processing algorithm, which combines discrete Fourier transform on the premise of ensuring the convergence of a check mode decomposition algorithm, and due to the iterative filterability of the empirical mode decomposition algorithm, the decomposed components have no direct correlation with the next layer of components, so that most of spectral energy is in different frequency bands when signals are extracted, and when the same bandwidth as that of wavelet analysis is achieved, the number of required decomposition layers is reduced, thereby minimizing the energy loss.
Compared with the prior art, the multi-resolution audio signal processing method has the following beneficial effects:
1. the method can be used for carrying out self-adaptive signal processing analysis aiming at nonlinearity, and has wider application range.
2. The wavelet kernel does not need to be set in advance, the problem of wavelet optimal basis search does not exist, and the method has a stable analysis effect on unsteady signals.
3. For the effect achieved by the wavelets of the same grade, the method has less decomposition times and less energy loss.
Example two
On the basis of the method for processing a multi-resolution audio signal provided in the first embodiment, a second embodiment of the present invention provides a device for processing a multi-resolution audio signal, as shown in fig. 10: the multi-resolution audio signal processing apparatus includes:
a first transform module 10, configured to perform discrete fourier transform on the decomposed sub-signals;
the inserting module 11 is configured to insert a zero point into the sub-signal after the discrete fourier transform;
the first inverse transformation module 12 is configured to perform inverse discrete fourier transformation on the sub-signals with zero inserted;
the first decomposition module 13 is configured to perform a second-stage decomposition on the inverse discrete fourier transformed sub-signal;
a second transform module 14, configured to perform discrete fourier transform on the sub-signals after the second-stage decomposition;
and a removing module 15, configured to remove the coefficient of the second-stage sub-signal after the discrete fourier transform at the zero insertion position, so as to obtain a shortened second-stage sub-signal.
And a second inverse transform module 16 for performing inverse discrete fourier transform on the shortened second-stage sub-signals.
The inserting module 11 inserts zero points into the sequences of the sub-signals after the discrete fourier transform, and the number of the sequences after the zero points are conjugate and symmetric.
Further, the multi-resolution audio signal processing apparatus further includes:
and the second decomposition module 17 is used for decomposing the superior signal.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (6)
1. A multi-resolution audio signal processing method, comprising:
performing discrete Fourier transform on the sub-signals to obtain a first-stage DFT sequence;
inserting a zero point into the first-stage DFT sequence to obtain a first-stage sequence after the zero point is inserted;
performing inverse discrete Fourier transform on the first-stage sequence after the zero point is inserted to obtain a first-stage sequence after inverse discrete Fourier transform;
decomposing the first-stage sequence after the inverse discrete Fourier transform to obtain a plurality of second-stage eigenmode functions;
performing discrete Fourier transform on the intrinsic mode function of each second level to obtain a DFT sequence of the intrinsic mode function of the second level;
removing the coefficient of the second-level eigenmode function at the zero insertion position of the DFT sequence of each second-level eigenmode function to obtain a shortened DFT sequence of the second-level eigenmode function;
and performing inverse discrete Fourier transform on the DFT sequence of each shortened second-stage eigenmode function to obtain a final second-stage eigenmode function.
2. The multi-resolution audio signal processing method according to claim 1, characterized in that: and the number of elements in the first-stage sequence after zero insertion is conjugate and symmetrical.
3. The multi-resolution audio signal processing method according to claim 1, characterized in that: before the discrete fourier transform of the sub-signals, the method further comprises:
the original signal is decomposed into a plurality of sub-signals.
4. A multi-resolution audio signal processing apparatus, comprising:
the first transformation module is used for carrying out discrete Fourier transformation on the decomposed sub-signals;
the inserting module is used for inserting zero points into the sub-signals after the discrete Fourier transform;
the first inverse transformation module is used for carrying out inverse discrete Fourier transformation on the sub-signals inserted with the zero points;
the first decomposition module is used for carrying out second-stage decomposition on the sub-signals subjected to the inverse discrete Fourier transform;
the second transformation module is used for carrying out discrete Fourier transformation on the subsignals subjected to the second-stage decomposition;
the removing module is used for removing the coefficient of the second-level sub-signal after the discrete Fourier transform at the zero point insertion position to obtain a shortened second-level sub-signal;
and the second inverse transformation module is used for carrying out inverse discrete Fourier transformation on the shortened second-stage sub-signals.
5. The multi-resolution audio signal processing apparatus according to claim 4, wherein: and the inserting module is used for inserting the zero point of the sub-signals after the discrete Fourier transform into the sequence, and the number of elements in the sequence after the zero point is inserted is conjugate and symmetrical.
6. The multi-resolution audio signal processing apparatus according to claim 4, wherein: the multi-resolution audio signal processing apparatus further includes:
and the second decomposition module is used for decomposing the superior signal.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108630193A (en) * | 2017-03-21 | 2018-10-09 | 北京嘀嘀无限科技发展有限公司 | Audio recognition method and device |
| CN109567743A (en) * | 2018-10-24 | 2019-04-05 | 加康康健有限公司 | A kind of signal reconfiguring method based on EMD, device, terminal device and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102937477A (en) * | 2012-11-06 | 2013-02-20 | 昆山北极光电子科技有限公司 | Bi-spectrum analysis method for processing signals |
| CN103209036A (en) * | 2013-04-22 | 2013-07-17 | 哈尔滨工程大学 | Transient signal detection method based on Hilbert-Huang transform (HHT) double noise reduction |
-
2015
- 2015-12-30 CN CN201511030282.0A patent/CN106128469A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102937477A (en) * | 2012-11-06 | 2013-02-20 | 昆山北极光电子科技有限公司 | Bi-spectrum analysis method for processing signals |
| CN103209036A (en) * | 2013-04-22 | 2013-07-17 | 哈尔滨工程大学 | Transient signal detection method based on Hilbert-Huang transform (HHT) double noise reduction |
Non-Patent Citations (1)
| Title |
|---|
| WEICHAO KUANG等: "Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach", 《DIGITAL SIGNAL PROCESSING》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108630193A (en) * | 2017-03-21 | 2018-10-09 | 北京嘀嘀无限科技发展有限公司 | Audio recognition method and device |
| CN108630193B (en) * | 2017-03-21 | 2020-10-02 | 北京嘀嘀无限科技发展有限公司 | Voice recognition method and device |
| CN109567743A (en) * | 2018-10-24 | 2019-04-05 | 加康康健有限公司 | A kind of signal reconfiguring method based on EMD, device, terminal device and storage medium |
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