CN109817229B - Single-bit audio compression transmission and reconstruction method assisted by superposition characteristic information - Google Patents
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Abstract
The invention discloses a single-bit audio compression transmission and reconstruction method assisted by superposition characteristic information, comprising the following steps of S1: carrying out feature processing on the sparse audio signal to obtain feature information; step S2: performing spread spectrum processing on the characteristic information to obtain spread spectrum characteristic vectors; step S3: carrying out single-bit compression processing on the sparse audio signal to obtain a 1-bit compression signal; step S4: carrying out weighted superposition processing on the spread spectrum eigenvector and the 1-bit compressed signal to obtain a sending signal; step S5: sending out the sending signal, receiving the sending signal at a receiving end, wherein the receiving signal is a signal with noise; step S6: carrying out recovery processing on the signal with noise to obtain recovery characteristic information and recovery 1-bit compression information; step S7: the sparse audio signal is reconstructed by using the recovery characteristic information assisted reconstruction algorithm, so that the reconstruction precision of the single-bit audio signal is effectively improved under the condition that the frequency spectrum resource of a transmission system is not increased.
Description
Technical Field
The invention relates to the field of audio compression transmission and reconstruction, in particular to a single-bit audio compression transmission and reconstruction method assisted by superposition characteristic information.
Background
Compressed Sensing (CS) technology has been widely used in the field of audio signal processing, and has made substantial progress. In practice, however, the compressed signal needs to be quantized before it is encoded. In order to relieve the hardware pressure of the AD converter and improve the data storage efficiency and the data transmission rate, single-bit compression sensing is further applied to audio signal processing.
However, existing single-bit compressed sensing reconstruction algorithms such as a Fixed-point continuous FPC (FPC) algorithm, a symbol Matching Pursuit (MSP) algorithm, a constrained Step convergence (RSS) algorithm, and a Binary Iterative Hard Threshold (BIHT) algorithm are not specifically proposed for single-bit reconstruction of sparse audio signals, and then the particularity of the audio signals is not considered, and the non-zero element position index of the sparse audio signals is not fully utilized, so that the reconstruction accuracy of the sparse audio signals is limited.
Although prior studies propose to assist the reconstruction of a single-bit quantized signal using support set information of the signal, the reconstruction performance of a single-bit compressed signal can be further improved. However, in the conventional transmission process of the single-bit compressed audio signal, the transmission of the supporting set information of the audio signal will occupy a certain spectrum resource, and the transmission cost is increased. Therefore, an effective method is needed to alleviate the contradiction between spectrum resources and reconstruction accuracy.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a single-bit audio compression transmission and reconstruction method assisted by superposition characteristic information, solves the problems that in the traditional transmission process of single-bit compressed audio signals, the transmission of support set information of the audio signals occupies certain frequency spectrum resources, increases the transmission cost, and effectively improves the reconstruction precision of the audio signals.
The technical scheme adopted by the invention is as follows: the single-bit audio compression transmission and reconstruction method assisted by the superposition characteristic information comprises the following steps:
step S1: carrying out feature processing on the sparse audio signal to obtain feature information;
step S2: performing spread spectrum processing on the characteristic information to obtain spread spectrum characteristic vectors;
step S3: carrying out single-bit compression processing on the sparse audio signal to obtain a 1-bit compression signal;
step S4: carrying out weighted superposition processing on the spread spectrum eigenvector and the 1-bit compressed signal to obtain a sending signal;
step S5: sending out the sending signal, receiving the sending signal at a receiving end, wherein the receiving signal is a signal with noise;
step S6: carrying out recovery processing on the signal with noise to obtain recovery characteristic information and recovery 1-bit compression information;
step S7: and reconstructing a sparse audio signal by using the recovery characteristic information to assist a reconstruction algorithm.
Preferably, step S1 includes the following sub-steps:
step S11: setting the sparsity of a sparse audio signal x as K and the length as N;
step S12: extracting the position index of a non-zero element of the sparse audio signal x to obtain a support set omega with the length of K;
step S13: finding the front L of the sparse audio signal x in a support set omega of length K1The position index of the maximum element of the amplitude value is obtained to obtain the length L2Part of the supporting set of
Wherein, L is more than or equal to 01≤K,L2＝L1；
Step S14: for the length L2Part of the supporting set of
Code modulation is carried out to obtain the length L3Is 1 or-1, wherein L3＜M；
Coded modulation of step S14 into
Step S141: assembling the partial supports of length l
The decimal number in the sequence is converted into r-bit binary number, the length of the generated sequence after conversion is L, and L is rl;
step S142: an element "1" in the sequence is mapped to a "1" and an element "0" is mapped to an element "-1".
Preferably, the expression of the single-bit compression process of step S3 is:
y＝sign(Φx)
in the formula, y represents a 1-bit compressed signal with the length of M, phi represents a pre-stored M multiplied by N measurement matrix phi, and x represents a sparse audio signal with the length of N.
Preferably, step S4 includes the following sub-steps:
step S41: the spread spectrum characteristic information H with the length M is given to the weight value
The 1-bit compression signal y with the length M is given a weight value of
α is a weighting coefficient and satisfies 0 < α < 1, EsEnergy of the transmitted signal;
step S42: the characteristic information and the 1-bit compressed signal are weighted and superposed, and the formula of the weighted superposition is
Wherein H represents spreading characteristic information of length M, y represents a 1-bit compressed signal of length M, z represents a transmission signal of length M, α is a weighting coefficient and satisfies 0 < α < 1, and EsIs the energy of the transmitted signal.
Preferably, step S6 includes the following sub-steps:
step S61: de-spreading the transmitted signal, the expression of the de-spreading process is
In the formula (I), the compound is shown in the specification,
representing a noisy signal of length M,
where n is a noise signal of length M, PhFor despreading signature information, QTTranspose for the spreading matrix Q;
step S62: de-spread characteristic information PhPerforming hard decision operation to obtain recovery characteristic information
Step S63: the characteristic information will be recovered
Spread spectrum processing is carried out to obtain recovery spread spectrum characteristic information with the length of M
Step S64: using noisy signals of length M
And recovering spread spectrum signature information
Calculating a despread compressed signal P of length My，
Step S65: de-spread compressed signal P of length MyPerforming hard decision operation to obtain a 1-bit compressed signal with length of M
Preferably, the expressions of the spreading processing of step S2 and step S63 are:
H＝Qh
wherein Q is a spreading matrix size of M × L and QTQ＝MILWherein L < M, ILIs an identity matrix of L × L, H is the characteristic information, and H is the spread spectrum characteristic vector.
Preferably, the hard decision process of steps S62 and S65 has the formula
In the formula (I), the compound is shown in the specification,
for the signal after hard decision processing, has a length of L3，PhFor despreading the signature information, length L3。
Preferably, step S7 includes the steps of:
step S71: for the recovery of characteristic information
Performing demodulation and decoding processing to obtain a recovered part of support set
Step S72 of supporting the collection with the restored portions
Recovery of 1-bit compressed signals from length M assisted by, and in combination with, a reconstruction algorithm
In-process reconstructing a sparse audio signal of length N
Preferably, the decoding of step S71 is demodulated into
Step S711: recovering the characteristic information with length L
Element "1" in (1)Shoot to "1", map element "-1" to "0";
step S712: converting each r binary number in the sequence with the length of L into a decimal number to obtain a recovery part support set with the length of L
And L ═ rl;
preferably, the reconstruction algorithm of step S72 includes the steps of:
step S721: input recovery 1-bit compressed signal
Measurement matrix phi ∈ RM×NSparsity K, recovery of partial supporting set
L is more than 0 and less than or equal to K, and the maximum iteration number iternum is greater than or equal to K;
step S722: initializing a residual vector x0＝ON×1The iteration time t is 0;
step S723: according to x, phi,
And gradient calculation formula
Calculate βt+1；
Step S724 according to βt+1And hard threshold mapping formula xt+1＝η(βt+1) Calculate xt+1；
Step S725: according to xt+1、βt+1、
And support set mapping formulas
Calculate xt+1；
Where ξ (-) is a support set mapping operatorNumber, will be collected
At vector βt+1The element amplitude of the middle index is assigned to the set
At xt+1The index of (1) is located;
step S726: calculating the number of non-zero elements
Let t be t + 1;
wherein the symbol | · | non-calculation0Operator 0 norm representing vector solving
Step S727: if t is less than iternum and nnz is more than 0, if yes, returning to step S723; if not, go to step S728;
step S728: according to the calculated xt+1Then calculate xt+1＝U(Xt+1)，
Wherein u (v) ═ v/| | v | | | grind2Symbol | · | non-conducting phosphor22, representing an operator 2 norm of the vector;
step S729: according to xt+1Computing a sparse audio signal
The single-bit audio compression transmission and reconstruction method assisted by the superposition characteristic information has the following beneficial effects:
compared with the traditional single-bit compression perception voice compression, the method considers the particularity of the audio signal, utilizes the partial position index of the non-zero elements of the sparse audio signal to assist reconstruction, and improves the reconstruction precision of the audio signal under the condition of not increasing the frequency spectrum overhead.
Drawings
Fig. 1 is a flow chart of a single-bit audio compression transmission and reconstruction method assisted by superposition characteristic information according to the invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the method for single-bit audio compression transmission and reconstruction assisted by overlay feature information includes the following steps:
step S1: carrying out feature processing on the sparse audio signal to obtain feature information;
step S2: performing spread spectrum processing on the characteristic information to obtain spread spectrum characteristic vectors;
step S3: carrying out single-bit compression processing on the sparse audio signal to obtain a 1-bit compression signal;
step S4: carrying out weighted superposition processing on the spread spectrum eigenvector and the 1-bit compressed signal to obtain a sending signal;
step S5: sending out the sending signal, and receiving the sending signal at a receiving end, wherein the receiving signal is a signal with noise;
step S6: carrying out recovery processing on the signal with noise to obtain recovery characteristic information and recovery 1-bit compression information;
step S7: and reconstructing a sparse audio signal by using the recovery characteristic information to assist a reconstruction algorithm.
Step S1 of the present embodiment includes the following substeps:
step S11: setting the sparsity of a sparse audio signal x as K and the length as N;
step S12: extracting the position index of a non-zero element of the sparse audio signal x to obtain a support set omega with the length of K;
step S13: finding the front L of the sparse audio signal x in a support set omega of length K1Obtaining the position index with the maximum amplitude valueLength L2Part of the supporting set of
Wherein, 0 is more than L1≤K,L2＝L1；
Step S14: for the length L2Part of the supporting set of
Code modulation is carried out to obtain the length L3Is determined, wherein L3＜M。
The sparse audio signal in step S1 in this embodiment is a sparse audio signal obtained by transforming a discrete audio signal from a time domain signal to a frequency domain signal by a time-frequency transform method, and setting the signal amplitude lower than a masking threshold to zero according to a masking effect.
L of this embodiment1Set according to engineering experience and satisfy 0 < L1≤K；
In the embodiment of the present application, let:
x ═ 0,0,0,3.75,0,0,0,0, -2.67,0,0,0, -5.12,0,0,0,0,4.89,0,0,0,1.56,0,0,0,0], can give:
sparsity K is 5, support set Ω is {4,9,15,20,24 };
is provided with L13, then x is preceded by L1The maximum non-zero element of the 3 amplitude values is { -5.12,4.89,3.75}, and the corresponding position index is {15,20,4}, so that the partial support set is formed
Length L2＝L1＝3；
Step S14: for the length L2Part of the supporting set of
Code modulation is carried out to obtain the length L3Is determined, wherein L3＜M。
The expression of the single-bit compression processing of step S3 of the present embodiment is:
y＝sign(Φx)
in the formula, y represents a 1-bit compressed signal with the length of M, phi represents a pre-stored M multiplied by N measurement matrix phi, and x represents a sparse audio signal with the length of N. .
Wherein sign (. circle.) in this embodiment represents a {1, -1} sign function, i.e., a number greater than 0 is set to 1, and the remaining number is set to-1.
Step S4 of the present embodiment includes the following substeps:
step S41: the spread spectrum characteristic information H with the length M is endowed with a weight value of
The 1-bit compression signal y with the length M is given a weight value of
α is a weighting coefficient and satisfies 0 < α < 1, EsEnergy of the transmitted signal;
step S42: the characteristic information and the 1-bit compressed signal are weighted and superposed, and the formula of the weighted superposition is
Wherein H represents spreading characteristic information of length M, y represents a 1-bit compressed signal of length M, z represents a transmission signal of length M, α is a weighting coefficient and satisfies 0 < α < 1, and EsIs the energy of the transmitted signal.
Step S6 of the present embodiment includes the following substeps:
step S61: de-spreading the transmitted signal, the expression of the de-spreading process is
In the formula (I), the compound is shown in the specification,
representing a noisy signal of length M,
where n is a noise signal of length M, PhFor despreading signature information, QTTranspose for the spreading matrix Q;
step S62: de-spread characteristic information PhPerforming hard decision operation to obtain recovery characteristic information
Step S63: the characteristic information will be recovered
Spread spectrum processing is carried out to obtain recovery spread spectrum characteristic information with the length of M
Step S64: using noisy signals of length M
And recovering spread spectrum signature information
Calculating a despread compressed signal P of length My，
Step S65: de-spread compressed signal P of length MyPerforming hard decision operation to obtain a 1-bit compressed signal with length of M
In this embodiment, the expressions of the spreading processing in step S2 and step S63 are:
H＝Qh
wherein Q is a spreading matrix size of M × L and QTQ＝MILWherein L < M, ILIs an identity matrix of L × L, h isAnd H is a spread spectrum eigenvector.
In this embodiment, the hard decision processing of step S62 and step S65 has the following formula
In the formula (I), the compound is shown in the specification,
for the signal after hard decision processing, has a length of L3，PhFor despreading the signature information, length L3。
The hard decision operation of this embodiment is to assign P to PhThe elements larger than 0 are set as 1, and the rest elements are set as-1;
in the embodiment of the present application, let:
Ph＝[0.25,-0.36,1.58,-2.96,3.74,5.62,-0.02,1.23,0.85,-6.84]to PhPerforming hard decision operation to obtain recovery characteristic information sequence
Step S7 of the present embodiment includes the steps of:
step S71: for the recovery of characteristic information
Performing demodulation and decoding processing to obtain a recovered part of support set
Step S72 of supporting the collection with the restored portions
Recovery of 1-bit compressed signals from length M assisted by, and in combination with, a reconstruction algorithm
In-process reconstruction of sparse audio of length NSignal
The code modulation of step S14 of the present embodiment is
Step S141: assembling the partial supports of length l
The decimal number in the sequence is converted into r-bit binary number, the length of the generated sequence after conversion is L, and L is rl;
step S142: an element "1" in the sequence is mapped to a "1" and an element "0" is mapped to an element "-1".
The decoding demodulation of step S71 of the present embodiment is
Step S711: recovering the characteristic information with length L
The element "1" in (1) is mapped to "1", and the element "-1" is mapped to "0";
step S712: converting each r binary number in the sequence with the length of L into a decimal number to obtain a recovery part support set with the length of L
And L ═ rl;
the reconstruction algorithm of step S72 of the present embodiment includes the following steps:
step S721: input recovery 1-bit compressed signal
Measurement matrix phi ∈ RM×NSparsity K, recovery of partial support sets
Maximum number of iterations iternum;
step S722: initializing a residual vector x0＝ON×1The iteration time t is 0;
step S723: according to X, phi,
And gradient calculation formula
Calculate βt+1；
Step S724 according to βt+1And hard threshold mapping formula xt+1＝η(βt+1) Calculate xt+1；
η (-) is a hard threshold mapping operation notation, i.e. reserved βt+1The first K maximum elements in the middle are set as 0;
in the embodiment of the present application, let:
βt+1＝[-0.92,1.10,-7.02,4.33,10.36,5.48,-0.77,-2.25,3.66,5.90,6.75,6.96,9.09,-2.05,-1.41,-6.84,-3.49,-3.04,-2.64,1.22]k is 10, we can derive:
xt+1＝[0,0,-7.02,4.33,10.36,5.48,0,0,3.66,5.90,6.75,6.96,9.09,0,0,-6.84,0,0,0,0]。
step S725: according to xt+1、βt+1、
And support set mapping formulas
Calculate xt+1；
ξ (-) is a symbol of a support set mapping operation, i.e., a set
At vector βt+1The element amplitude of the middle index is assigned to the set
At xt+1The index of (1) is located;
in the embodiment of the present application, let:
xt+1＝[0,0,-7.02,4.33,10.36,5.48,0,0,3.66,5.90,6.75,6.96,9.09,-2.05,0,-6.84,0,0,0,0]。
step S726: calculating the number of non-zero elements
Let t be t + 1;
wherein the symbol | · | non-calculation0An operator 0 norm representing the vector is solved;
step S727: if t is less than iternum and nnz is more than 0, returning to step S723; if not, go to step S727;
step S728: x of S725 calculated according to the stept+1Then calculate xt+1＝U(xt+1)，
Wherein u (v) ═ v/| | v | | | grind2Symbol | · | non-conducting phosphor22, representing an operator 2 norm of the vector;
step S729: according to xt+1Computing a sparse audio signal
Claims (1)
1. A single-bit audio compression transmission and reconstruction method assisted by superposition characteristic information is characterized by comprising the following steps:
step S1: carrying out feature processing on the sparse audio signal to obtain feature information;
step S2: carrying out expansion processing on the feature information to obtain an expanded feature vector;
step S3: carrying out single-bit compression processing on the sparse audio vector to obtain a 1-bit compression signal;
step S4: carrying out weighted superposition processing on the expansion characteristic vector and the 1-bit compressed signal to obtain a sending signal;
step S5: sending out the sending signal;
step S6: carrying out recovery processing on a sending signal received by a receiving end to obtain recovery characteristic information and recovery 1-bit compression information;
step S7: carrying out auxiliary reconstruction algorithm processing on the recovery characteristic information to obtain a sparse audio signal;
the step S1 includes the following sub-steps:
step S11: setting the sparsity of a sparse audio signal X as K and the length as N;
step S12: extracting the position index of a non-zero element of the sparse audio signal X to obtain a support set omega with the length of K;
step S13: finding the front L of the sparse audio signal X in a support set omega of length K1Obtaining the position index with the maximum amplitude value and the length of L2Part of the supporting set of
Wherein 0 < L1≤K；
Step S14: for the length L2Part of the supporting set of
Code modulation is carried out to obtain the length L3Is 1 or-1, wherein L3＜M；
The expression of the single-bit compression processing of step S3 is:
y＝sign(Φx)
in the formula, y represents a 1-bit compressed signal, phi represents a pre-stored M multiplied by N measurement matrix phi, and x represents a sparse audio signal;
the step S4 includes the following sub-steps:
step S41: the extension characteristic information H with the length M is given a weight value of
The 1-bit compressed signal y is given a weight of
α is anWeight coefficient satisfying 0 < α < 1, EsEnergy of the transmitted signal;
step S42: the characteristic information and the 1-bit compressed signal are weighted and superposed, and the formula of the weighted superposition is
Wherein H represents a transmission signal Z having a length M, y represents a 1-bit compressed signal, α is a weighting coefficient and satisfies 0 < α < 1, and EsEnergy of the transmitted signal;
the step S6 includes the following sub-steps:
step S61: de-spreading the transmitted signal, the expression of the de-spreading process is
In the formula (I), the compound is shown in the specification,
it is indicated that the signal is transmitted,
where n is a noise signal of length M, PhFor despreading signature information, QTIs the transposition of the spreading matrix;
step S62: de-spread characteristic information PhPerforming hard decision operation to obtain recovery characteristic information
Step S63: the characteristic information will be recovered
Performing extension processing to obtain recovery spread spectrum characteristic information with length of M
Step S64: to noise signal of length M
And recovering spread spectrum signature information
Performing despreading and compression processing to obtain a despread and compressed signal P with length My，
Step S65: de-spread compressed signal P of length MyPerforming hard decision operation to obtain a 1-bit compressed signal with length of M
The expressions of the spreading processing of step S2 and step S63 are:
H＝Qh
wherein Q is a spreading matrix size of M × L and QTQ＝MILWherein L < M, ILThe matrix is an identity matrix of L × L, H is characteristic information, and H is an expansion characteristic vector;
the hard decision operation of step S62 and step S65 has the formula
In the formula (I), the compound is shown in the specification,
the signal is processed by hard decision, and y is a compressed signal;
the step S7 includes the following steps:
step S71: for the recovery of characteristic information
Code modulation processing is carried out to obtain a recovered part of the support set
Step S72, supporting the collection for the recovered part
Assisted and simultaneously recovering 1-bit compressed signal with length M
Reconstructing a sparse audio signal of length N
The coded modulation of step S14 and step S71 is
Step S141: assembling partial supports of length l "
Decimal elements in the sequence are converted into r-bit binary numbers, the length of a generated sequence after conversion is L, and L is rl;
step S142: mapping an element "1" in the sequence to "1", and mapping an element "0" to an element "-1";
the reconstruction algorithm of step S72 includes the following steps:
step S721: input recovery 1-bit compressed signal
Measurement matrix phi ∈ RM×NSparsity K, recovery of partial supporting set
Maximum number of iterations iternum;
step S722: initializing a residual vector x0＝ON×1The iteration time t is 0;
step S723: according to x, phi,
And gradient calculation formula
Calculate gradient βt+1；
Step S724, according to the gradient βt+1And hard threshold mapping formula xt+1＝η(βt+1) Calculating a hard threshold xt+1；
Step S725: calculating the number of non-zero elements
Wherein the symbol | | | purple0An operator 0 norm for calculating a vector is expressed, and t is t + 1;
step S726: if t is less than iternum and nnz is more than 0, returning to step S723; if not, go to step S727;
step S727: calculating x from tt+1＝U(xt+1)，
Wherein u (v) ═ v/| | v | | | grind2Symbol | | | non-conducting phosphor22, representing an operator 2 norm of the vector;
step S728: according to xt+1Computing a sparse audio signal
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012155233A (en) * | 2011-01-28 | 2012-08-16 | Jvc Kenwood Corp | Information transmission device |
| CN103684472A (en) * | 2013-12-29 | 2014-03-26 | 哈尔滨工业大学 | Reconfiguration method of adaptive signal of 1-Bit sparse level based on compression perception |
| CN105206277A (en) * | 2015-08-17 | 2015-12-30 | 西华大学 | Voice compression method base on monobit compression perception |
| CN108962265A (en) * | 2018-05-22 | 2018-12-07 | 西华大学 | A kind of Speech Signal Compression storage and reconstructing method based on superposition sequence |
-
2019
- 2019-03-14 CN CN201910193583.7A patent/CN109817229B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012155233A (en) * | 2011-01-28 | 2012-08-16 | Jvc Kenwood Corp | Information transmission device |
| CN103684472A (en) * | 2013-12-29 | 2014-03-26 | 哈尔滨工业大学 | Reconfiguration method of adaptive signal of 1-Bit sparse level based on compression perception |
| CN105206277A (en) * | 2015-08-17 | 2015-12-30 | 西华大学 | Voice compression method base on monobit compression perception |
| CN108962265A (en) * | 2018-05-22 | 2018-12-07 | 西华大学 | A kind of Speech Signal Compression storage and reconstructing method based on superposition sequence |
Non-Patent Citations (3)
| Title |
|---|
| 《1-Bit压缩感知盲重构算法》;张京超等;《电子与信息学报》;20150331;第37卷(第3期);第567-573页 * |
| 《One-bit Compressive Sensing with partial support》;Phillip North et al.;《2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)》;20160121;第349-352页 * |
| 《单比特压缩感知帧定时同步》;王维等;《计算机工程与应用》;20180831;第54卷(第15期);第57-61页 * |
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Application publication date: 20190528 Assignee: Chengdu Tiantongrui Computer Technology Co.,Ltd. Assignor: XIHUA University Contract record no.: X2023510000028 Denomination of invention: A Single Bit Audio Compression Transmission and Reconstruction Method Assisted by Superimposed Feature Information Granted publication date: 20200922 License type: Common License Record date: 20231124 |