# The New Approach Research on Singing Voice Detection Algorithm Based on Enhanced Reconstruction Residual Network

Authors: Lilin Liu
Journal: Journal of Mathematics
 (2022)

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## Abstract

With the development of Internet technology, multimedia information resources are increasing rapidly. Faced with the massive resources in the multimedia music library, it is extremely difficult for people to find the target music that meets their needs. How to realize computer analysis and perceive users’ needs for music resources has become the goal of the future development of human-computer interaction capabilities. Content-based music information retrieval applications are mainly embodied in the automatic classification and recognition of music. Traditional feedforward neural networks are prone to lose local information when extracting singing voice features. For this reason, on the basis of fully considering the impact of information persistence in the network propagation process, this paper proposes an enhanced two-stage super-resolution reconstruction residual network which can effectively integrate the learned features of each layer while increasing the depth of the network. The first stage of reconstruction is to complete the hierarchical learning of singing voice features through dense residual units to improve the integration of information. The second stage of reconstruction is mainly to perform residual relearning on the high-frequency information of the singing voice learned in the first stage to reduce the reconstruction error. In the middle of these two stages, the model introduces feature scaling and expansion convolution to achieve the dual purpose of reducing information redundancy and increasing the receptive field of the convolution kernel. A monophonic singing voice separation based on the high-resolution neural network is proposed. Because the high-resolution network has parallel subnetworks with different resolutions, it also has original resolution representations and multiple low-resolution representations, avoiding information loss caused by serial network downsampling effects and repeating multiple feature fusions to generate new semantic representations, allowing for the learning of comprehensive, high-precision, and highly abstract features. In this article, a high-resolution neural network is utilized to model the time spectrogram in order to correctly estimate the real value of the anticipated time-amplitude spectrograms. Experiments on the dataset MIR-1K show that compared with the current leading SH-4Stack model, the method in this paper has improved SDR, SIR, and SAR indicators for measuring the separation performance, confirming the effectiveness of the algorithm in this paper.


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## Body

## 1. Introduction

Multimedia technology changes with each passing day, constantly enriching people’s daily lives and work. As an important part of multimedia technology, audio technology affects people’s lives all the time. Since human civilization, music has been an important part of people’s spiritual culture [1]. It is a special language for people to place spiritual ideals, express their thoughts and feelings, and achieve mutual exchanges. It is also the crystallization of the human’s great wisdom. The creation, expression, understanding, and appreciation of music are the most basic spiritual activities of mankind [2]. In the advancement of human civilization, music has inherited a rich culture and history. It has always been an indispensable part of human life for thousands of years. Its meaning, form of existence, and mode of transmission are in a new era. Driven by technology, there will be new interpretations [3]. With the vigorous promotion of network technology, the amount of online music data is increasing day by day, and the demand for analysis, retrieval, and processing of music information has become increasingly prominent [4]. As one of the hotspots in the field of signal and information processing, music separation is an important part of music technology research [5].It is common for one singer to sing many different songs in singer search and song search, and one song has been sung by many singers. The separation of singing voice can improve the accuracy of singer identification [6]. Because in music information retrieval, the effect of direct retrieval is often unsatisfactory. If it can be separated, the retrieval efficiency will be greatly improved without mutual interference, and many complex multitone music processing can be avoided. Technology simplifies the problem even more. Similarly, during music information analysis (preprocessing, pitch, melody extraction, etc.), the mutual interference between music and singing also leads to inaccurate information analysis [7]. A good separation system will bring great convenience to this. In automatic lyrics recognition, it is usually required to input a single singing voice, which is not in line with reality. Because the singing voice has background music, such a requirement can be achieved. Correcting lyrics for singing voices is a critical and tedious step for applications such as karaoke. Automatic lyrics correction will bring great convenience, but automatic lyrics correction task becomes difficult when background music exists.The auditory characteristics of the singer’s voice signal are described in detail, which is also an important part of the system, and the analysis and calculation methods of each auditory characteristic are introduced. The super-resolution reconstruction model based on the deep residual network is one of the research trends in the past two years. This article first introduces the basic principles of the deep residual network and analyzes the advantages and disadvantages of the existing super-resolution model based on the residual network. After analyzing and inspired by densely connected networks, this paper combines the advantages of deep residual networks and densely connected networks to propose an enhanced two-stage reconstruction residual network and introduces two deep learning features. The suggested two-stage residual deep convolutional neural network technique is subjected to comparative experimentation and analysis in this study. The experimental environment, data preparation, and singing voice separation evaluation indicators were first introduced; second, experimental schemes based on the high-resolution network separation of songs, phase optimization algorithms, spectrum amplitude constraints, and data expansion were designed; finally, the separation performance of each algorithm was compared and analyzed.


## 2. Related Work

In theory, deep learning belongs to the category of machine learning [8]. Machine learning completes the learning of the model by selecting appropriate features. The result depends on the choice of features. However, manually selecting features is very time consuming and labor intensive, and requires a lot of prior knowledge. Therefore, the introduction of deep learning has greatly promoted the development of machine learning, among which the most widely used are visual problems, speech recognition, and text processing [9]. The application effect of deep learning in computer vision is remarkable. In addition to common classification problems, the accuracy of tasks such as face recognition, object detection, and singing voice super-resolution has also been greatly improved [10].Convolutional neural network (CNN) is a common deep learning architecture inspired by biological natural visual cognitive mechanisms. Researchers found that their unique network structure can effectively reduce the complexity of the feedback neural network when studying neurons used for local sensitivity and direction selection in the cat brain cortex and then proposed CNN [11]. The new recognition machine proposed by related scholars is the first realization of the convolutional neural network. Later, more scientific researchers improved the network [12, 13]. Researchers established the modern structure of CNN [14]. They designed a multilayer artificial neural network, named “LeNet-5,” which can classify handwritten digits. Like other neural networks, LeNet-5 can also be trained using the back propagation (BP) algorithm. CNN can obtain the effective feature representation of the original singing voice, which enables CNN to recognize the rules of vision directly from the original pixels [15]. However, due to the lack of large-scale training data at that time and the computing power of the computer that could not keep up, LeNet-5 was not ideal for handling complex problems.Based on the research on the perception mechanism of the human auditory system, related scholars proposed the concept of “synchronization string” [16, 17]. They used the Gammatone filter bank to simulate the structure of the human cochlea to process mixed sounds and then obtained a series of time-frequency units. The degree of correlation between adjacent parts classifies the synchronization string to which the sound signal belongs, and analyzes the mixed sound step by step in time sequence. Researchers have proposed a new CASA system, the “blackboard” model, based on the physiological and psychological characteristics of the human auditory system, using many different criteria to extract and recombine sound signals [18]. This model's organization and processing method of sound signals is a breakthrough innovation for CASA research, which can better realize the simulation of the human auditory system. Relevant researchers reported a neural network-based CASA system that detailed how the same sound source signal is separated into a sound stream based on several sound separation triggers [19]. A schema-driven phoneme repair model has been created by related researchers. This method can restore a sound signal by processing it via an imprecise speech recognition model and restoring the associated masking material. Related scholars have studied a model that uses harmonic characteristics to separate sounds, designs a filter according to the pitch frequency to simply and roughly separates each sound source, and then performs targeted enhancement and compression on the separated sound to highlight the target voice. Researchers proposed a speech separation algorithm based on sound localization and auditory masking effect, using binaural effect as the main sound separation clue, and combined with an ideal binary masking algorithm to separate mixed sounds [20].


## 3. Analysis of the Auditory Characteristics of Singing Voice Signals

### 3.1. Pretreatment

#### 3.1.1. Sampling

They are all digital music, with a certain sampling frequency and encoding mechanism, whether they are songs on the Internet or CDs. However, the encoding technique and sample frequency of music have a significant impact on the processing of music data in the experiment. As a result, before framing, the music signal’s sampling frequency and encoding technique must be unified. In most applications, digital music does not require a too-high sampling rate, and a too-high sampling rate will increase the complexity of the calculation. In this paper, the music signal is uniformly converted into a WAV format, which is convenient for analysis and processing in the MATLAB environment. For the sampling frequency, the unified standard 22050 Hz in the MIR research field is adopted, which also improves the execution efficiency of the algorithm. The test music signal (44.1 kHz) can be downsampled without losing the basic music recognition characteristics, and the recognition effect is less.


#### 3.1.2. Framing

According to this characteristic, considering the time continuity and short-term stability of the music signal, the music signal needs to be framed after sampling. The length of the frame will directly affect the feature extraction and recognition results. Suppose the sampling period isT = 1/f, there is the following relationship between the window length N and the frequency Δf:(1)Δf=NTs−1.When the sampling periodTs is constant, the frequency resolution Δf is inversely proportional to the frame length N. When N increases, Δf will decrease.


#### 3.1.3. Preemphasis

Normally, hardware or software can be used for preemphasis. This article uses software preemphasis in the MATLAB environment. The transfer function used is(2)Hz=1−1−az−1.Among them,a is called the preemphasis coefficient.


#### 3.1.4. Add Windows

The purpose of windowing is to divide the speech signal into frames, and the frame length is denoted asN. For each frame, the time window function ω (n) is multiplied with the original speech signal s (n). This frame contains the speech signal sequence of N points in the sample, and N is the window length. Generally, there are two commonly used window functions in the windowing process.The rectangular window function is(3)wn=0,n∈0,N−1,1,others.The Hamming window function is(4)wHn=1,others,0.42−0.38sin2πn−1N,n∈0,N−1.It can be seen from the above two formulas that the length of the window functionN is used to calculate the amplitude of the signal. When the window length is extremely large, the window function is approximately equivalent to a narrow low-pass filter, and the length of N is equal to several pitch periods. During this period of time, the short-term information of the signal changes very slowly, and the details of the waveform are often ignored. Conversely, if N is extremely small, this value may be equivalent to or even less than a gene period for a pitch period, and the short-term energy change of the signal will fluctuate according to the change of the signal waveform. If N is too small, the bandwidth of the filter will become wider, and smooth short-term information cannot be obtained. Therefore, the length of the window function should be selected appropriately. It is generally believed that 1 to 7 pitch periods can fully reflect the change characteristics that should be in a speech frame. But the pitch period varies widely for different people, so the choice of N is more difficult. Generally, when the sampling frequency is 10 kHz, the window length is most appropriate to choose from 100 to 200 points, that is, the duration of 15 mins to 30 mins.


### 3.2. Mel Cepstral Coefficient

Mel frequency cepstral coefficient (MFCC) is a cepstral coefficient based on Mel frequency. To obtain the frequency spectrum of the signal, the discrete Fourier transform is first used to calculate the short-time Fourier transform; second, the obtained logarithmic energy spectrum is filtered withM Mel triangular filter banks; finally, the output vector is subjected to the discrete cosine transform DCT, with the first N coefficients taken. The discrete cosine transform is actually the process of inverse Fourier transform; that is, the cepstral coefficients are obtained.The reason why MFCC reflects the auditory characteristics of the human ear is that the Mel filter simulates the cochlea model of the human ear. According to the cochlea function, the cochlea's filtering function is similar to the function of the Mel filter, which mainly presents a relationship on a logarithmic scale. It is basically linear below the lower 1000 Hz, and the frequency greater than 1000 Hz is shown as a logarithmic relationship; that is, the human ear recognizes low-frequency sounds more easily than high-frequency sounds. The Mel frequency of the Mel frequency filter bank similar to the cochlear model is expressed as follows:(5)fmel=2400⋅logf300+1.The output formula of the Mel filter is(6)Hmk=1,k≥fm+1,k−fm+1fm+1−fm−1,fm<k<fm+1,fm−1−k−1fm−fm−1,fm−1<k<fm,0,k≤fm−1.Among them,M represents the number of channels of the filter and f(m) represents the center frequency. The definition of f (m) is(7)fm=NFS−1Fmel−1Fmelf1−mFmelfhM−1−Fmelfl.Among them,fh and fl represent the highest and lowest frequencies, N represents the frame length, and FS represents the sampling frequency.


### 3.3. Mel Transformation of Linear Prediction Coefficients

The Mel frequency is a simulation of the frequency characteristics of the human cochlea, in which the Mel filter plays a decisive role in this simulation performance. Therefore, linear prediction Mel-frequency cepstral coefficient (LPMCC) is based on linear prediction cepstral coefficient (LPCC), improving its Mel frequency and achieving better results in recognition. LPCC is often used as a characteristic parameter of speech recognition. It has a good performance in speaker recognition technology, but it has a poor effect on singer recognition. Therefore, LPCC is improved to enhance its performance. Next, the theoretical model of LPCC is introduced.We assume that the p-order linear prediction coefficient of the speech signal is calculated. We use the firstp samples of the speech signal to predict the current speech sample, so the p-order linear prediction value is obtained. The specific description is that the first p samples are linearly combined to predict the voice signal sample value at the next moment, and the prediction standard is that the error is minimized. That is, the predicted value of s(n) is(8)s⌢n=∏i=0p−1ai⋅sn−i−1,where s(n) is constructed with {ak}, which fits the data in the sense of least mean square. {ak} is the coefficient of p-order linear prediction. The forecast error is(9)en=sn−∏i=0p−1ai−1sn−i−1.We perform z-transformation on the above formula to obtain the transfer function of the prediction error sequence:(10)Az=1−∑i=0p−1ai−1z−k+1.According to the definition of p-order linear prediction, the sum of squaresE of all prediction errors of the speech frame is(11)E=∏n=p−1Nsn−1−∏i=0p−1ai−1sn−i−1.


## 3.1. Pretreatment

### 3.1.1. Sampling

They are all digital music, with a certain sampling frequency and encoding mechanism, whether they are songs on the Internet or CDs. However, the encoding technique and sample frequency of music have a significant impact on the processing of music data in the experiment. As a result, before framing, the music signal’s sampling frequency and encoding technique must be unified. In most applications, digital music does not require a too-high sampling rate, and a too-high sampling rate will increase the complexity of the calculation. In this paper, the music signal is uniformly converted into a WAV format, which is convenient for analysis and processing in the MATLAB environment. For the sampling frequency, the unified standard 22050 Hz in the MIR research field is adopted, which also improves the execution efficiency of the algorithm. The test music signal (44.1 kHz) can be downsampled without losing the basic music recognition characteristics, and the recognition effect is less.


### 3.1.2. Framing

According to this characteristic, considering the time continuity and short-term stability of the music signal, the music signal needs to be framed after sampling. The length of the frame will directly affect the feature extraction and recognition results. Suppose the sampling period isT = 1/f, there is the following relationship between the window length N and the frequency Δf:(1)Δf=NTs−1.When the sampling periodTs is constant, the frequency resolution Δf is inversely proportional to the frame length N. When N increases, Δf will decrease.


### 3.1.3. Preemphasis

Normally, hardware or software can be used for preemphasis. This article uses software preemphasis in the MATLAB environment. The transfer function used is(2)Hz=1−1−az−1.Among them,a is called the preemphasis coefficient.


### 3.1.4. Add Windows

The purpose of windowing is to divide the speech signal into frames, and the frame length is denoted asN. For each frame, the time window function ω (n) is multiplied with the original speech signal s (n). This frame contains the speech signal sequence of N points in the sample, and N is the window length. Generally, there are two commonly used window functions in the windowing process.The rectangular window function is(3)wn=0,n∈0,N−1,1,others.The Hamming window function is(4)wHn=1,others,0.42−0.38sin2πn−1N,n∈0,N−1.It can be seen from the above two formulas that the length of the window functionN is used to calculate the amplitude of the signal. When the window length is extremely large, the window function is approximately equivalent to a narrow low-pass filter, and the length of N is equal to several pitch periods. During this period of time, the short-term information of the signal changes very slowly, and the details of the waveform are often ignored. Conversely, if N is extremely small, this value may be equivalent to or even less than a gene period for a pitch period, and the short-term energy change of the signal will fluctuate according to the change of the signal waveform. If N is too small, the bandwidth of the filter will become wider, and smooth short-term information cannot be obtained. Therefore, the length of the window function should be selected appropriately. It is generally believed that 1 to 7 pitch periods can fully reflect the change characteristics that should be in a speech frame. But the pitch period varies widely for different people, so the choice of N is more difficult. Generally, when the sampling frequency is 10 kHz, the window length is most appropriate to choose from 100 to 200 points, that is, the duration of 15 mins to 30 mins.


## 3.1.1. Sampling

They are all digital music, with a certain sampling frequency and encoding mechanism, whether they are songs on the Internet or CDs. However, the encoding technique and sample frequency of music have a significant impact on the processing of music data in the experiment. As a result, before framing, the music signal’s sampling frequency and encoding technique must be unified. In most applications, digital music does not require a too-high sampling rate, and a too-high sampling rate will increase the complexity of the calculation. In this paper, the music signal is uniformly converted into a WAV format, which is convenient for analysis and processing in the MATLAB environment. For the sampling frequency, the unified standard 22050 Hz in the MIR research field is adopted, which also improves the execution efficiency of the algorithm. The test music signal (44.1 kHz) can be downsampled without losing the basic music recognition characteristics, and the recognition effect is less.


## 3.1.2. Framing

According to this characteristic, considering the time continuity and short-term stability of the music signal, the music signal needs to be framed after sampling. The length of the frame will directly affect the feature extraction and recognition results. Suppose the sampling period isT = 1/f, there is the following relationship between the window length N and the frequency Δf:(1)Δf=NTs−1.When the sampling periodTs is constant, the frequency resolution Δf is inversely proportional to the frame length N. When N increases, Δf will decrease.


## 3.1.3. Preemphasis

Normally, hardware or software can be used for preemphasis. This article uses software preemphasis in the MATLAB environment. The transfer function used is(2)Hz=1−1−az−1.Among them,a is called the preemphasis coefficient.


## 3.1.4. Add Windows

The purpose of windowing is to divide the speech signal into frames, and the frame length is denoted asN. For each frame, the time window function ω (n) is multiplied with the original speech signal s (n). This frame contains the speech signal sequence of N points in the sample, and N is the window length. Generally, there are two commonly used window functions in the windowing process.The rectangular window function is(3)wn=0,n∈0,N−1,1,others.The Hamming window function is(4)wHn=1,others,0.42−0.38sin2πn−1N,n∈0,N−1.It can be seen from the above two formulas that the length of the window functionN is used to calculate the amplitude of the signal. When the window length is extremely large, the window function is approximately equivalent to a narrow low-pass filter, and the length of N is equal to several pitch periods. During this period of time, the short-term information of the signal changes very slowly, and the details of the waveform are often ignored. Conversely, if N is extremely small, this value may be equivalent to or even less than a gene period for a pitch period, and the short-term energy change of the signal will fluctuate according to the change of the signal waveform. If N is too small, the bandwidth of the filter will become wider, and smooth short-term information cannot be obtained. Therefore, the length of the window function should be selected appropriately. It is generally believed that 1 to 7 pitch periods can fully reflect the change characteristics that should be in a speech frame. But the pitch period varies widely for different people, so the choice of N is more difficult. Generally, when the sampling frequency is 10 kHz, the window length is most appropriate to choose from 100 to 200 points, that is, the duration of 15 mins to 30 mins.


## 3.2. Mel Cepstral Coefficient

Mel frequency cepstral coefficient (MFCC) is a cepstral coefficient based on Mel frequency. To obtain the frequency spectrum of the signal, the discrete Fourier transform is first used to calculate the short-time Fourier transform; second, the obtained logarithmic energy spectrum is filtered withM Mel triangular filter banks; finally, the output vector is subjected to the discrete cosine transform DCT, with the first N coefficients taken. The discrete cosine transform is actually the process of inverse Fourier transform; that is, the cepstral coefficients are obtained.The reason why MFCC reflects the auditory characteristics of the human ear is that the Mel filter simulates the cochlea model of the human ear. According to the cochlea function, the cochlea's filtering function is similar to the function of the Mel filter, which mainly presents a relationship on a logarithmic scale. It is basically linear below the lower 1000 Hz, and the frequency greater than 1000 Hz is shown as a logarithmic relationship; that is, the human ear recognizes low-frequency sounds more easily than high-frequency sounds. The Mel frequency of the Mel frequency filter bank similar to the cochlear model is expressed as follows:(5)fmel=2400⋅logf300+1.The output formula of the Mel filter is(6)Hmk=1,k≥fm+1,k−fm+1fm+1−fm−1,fm<k<fm+1,fm−1−k−1fm−fm−1,fm−1<k<fm,0,k≤fm−1.Among them,M represents the number of channels of the filter and f(m) represents the center frequency. The definition of f (m) is(7)fm=NFS−1Fmel−1Fmelf1−mFmelfhM−1−Fmelfl.Among them,fh and fl represent the highest and lowest frequencies, N represents the frame length, and FS represents the sampling frequency.


## 3.3. Mel Transformation of Linear Prediction Coefficients

The Mel frequency is a simulation of the frequency characteristics of the human cochlea, in which the Mel filter plays a decisive role in this simulation performance. Therefore, linear prediction Mel-frequency cepstral coefficient (LPMCC) is based on linear prediction cepstral coefficient (LPCC), improving its Mel frequency and achieving better results in recognition. LPCC is often used as a characteristic parameter of speech recognition. It has a good performance in speaker recognition technology, but it has a poor effect on singer recognition. Therefore, LPCC is improved to enhance its performance. Next, the theoretical model of LPCC is introduced.We assume that the p-order linear prediction coefficient of the speech signal is calculated. We use the firstp samples of the speech signal to predict the current speech sample, so the p-order linear prediction value is obtained. The specific description is that the first p samples are linearly combined to predict the voice signal sample value at the next moment, and the prediction standard is that the error is minimized. That is, the predicted value of s(n) is(8)s⌢n=∏i=0p−1ai⋅sn−i−1,where s(n) is constructed with {ak}, which fits the data in the sense of least mean square. {ak} is the coefficient of p-order linear prediction. The forecast error is(9)en=sn−∏i=0p−1ai−1sn−i−1.We perform z-transformation on the above formula to obtain the transfer function of the prediction error sequence:(10)Az=1−∑i=0p−1ai−1z−k+1.According to the definition of p-order linear prediction, the sum of squaresE of all prediction errors of the speech frame is(11)E=∏n=p−1Nsn−1−∏i=0p−1ai−1sn−i−1.


## 4. Deep Convolutional Neural Network Model Based on Two-Stage Residuals

This section will give a detailed introduction to the information of each part of the two-stage progressive reconstruction residual network. Figure1 shows the overall framework of the network. The low-resolution singing voice to be reconstructed is preprocessed by the feature extraction layer, and then the extracted features are transported to the first reconstruction stage. The pseudo-high frequency is obtained through the local residual learning of each dense residual unit. The main work of the second reconstruction stage is to integrate the original singing voice and real high-frequency information to achieve global residual learning and finally output a high-resolution singing voice. The techniques of feature scaling and expansion convolution are also included between the two reconstruction phases to complete the conversion of pseudo-high-frequency information to actual high-frequency information, making the singing voice’s high-frequency component richer and more delicate.Figure 1
Two-stage super-resolution residual network framework diagram.


### 4.1. The First Stage of Reconstruction

In general, the first stage of the model is based on the local residual learning of multiple dense residual units to obtain the initial high-frequency information of the singing voice required for the reconstruction process because they have to be processed subsequently. So, it is called pseudo-high-frequency information.The model obtains the original features of the singing voice through the feature extraction layer. The feature extraction layer is composed of two convolutional layers, and the structure of each convolutional layer is the same as that of each layer in the dense residual unit. The first layer takes low-resolution singing voices as input, and the output features will be used in two parts. One is as the input of the next layer, and the other is to provide the global residual learning module. The second layer of the feature extraction layer receives the output of the previous layer and then performs a nonlinear mapping on the features, and the obtained output directly participates in the local residual learning process corresponding to each dense residual unit. The following formula can express the abstraction layer:(12)Y0=F0F−1x−1.Here,x is the input low-resolution singing voice, F−1 represents the first layer of the feature extraction layer, and F0 is the second layer. Then, based on the local residual learning of each dense residual unit, the characteristic is that the input of each dense residual unit is Y0, which ensures that the learned singing residuals are structurally consistent with the original singing voices.Considering that in the traditional network, information will inevitably lose a part of it through the forward transmission of each layer, ensuring the invariance of information in the process of dissemination and maintaining the robustness of information memory is the focus of this article. This article uses the cyclic unit of a memory block as the main component of the dense residual learning unit. The cyclic unit is made up of 6 residual building blocks (RBBs). The learning process of thedth residual block of the rth dense residual unit can be expressed as(13)Yd,r=FYd,r−1−Yd,r−1.Yd, r−1 and Yd, r are the input and output of this block, respectively, and ℱ is the residual function. In particular, ℱ contains two convolutional layers, each of which undergoes a preactivated BN-ReLU nonlinear mapping and then performs a 3 × 3 convolution operation:(14)FYd,r−1=Wm,r⋅ττYd,r−1Wm,r2.Wm, r1 and Wm, r are the weights of the first and second layers, respectively, and 𝜏 is the BN-ReLU operation. For the sake of simplicity, the offsets in all formulas in the text are omitted.The dense residual unit will comprehensively consider the hierarchical characteristics of each RBB when abstracting the features. Through this dense connection operation, the network will minimize the loss of information after the information passes through each layer. However, this also leads to a problem of a large amount of calculation. For example, suppose now that the output of each block hasG feature maps, so the output of each unit will have 6⋅G feature maps. If the number of G is large, for example, G = 64, then the number of feature maps of the final output G is 384, which requires a high amount of calculation for hardware. Therefore, the model in this article considers the use of information filters, which can reduce the dimension of Yd and achieve the purpose of information fusion. The followingdescribes the specific implementation process of information filters.In a dense residual unit, the information filter filters the output results of the current dense residual unit Yd so that it can not only reduce the corresponding calculation amount but also meet the reconstruction requirements, thus making the learning process simple. ETRN uses a 1 × 1 convolutional layer to act as an information filter.


### 4.2. Dilated Convolution

The comparison between dilated convolution and traditional convolution is shown in Figure2. After the completion of the first stage of reconstruction, the 6 dense residual units will generate 6 different levels of pseudo-high-frequency information, and then the pseudo-high frequency corresponding to the previous unit is added element-by-element to form the dense residual unit. Relearning the residuals is the second stage of reconstruction. After two different levels of pseudo-high frequency singing voice features are directly added, the model uses expanded convolution to process the fused data.Figure 2
Comparison of dilated convolution and traditional convolution.In traditional convolution, a 3 × 5 receptive field requires 15 convolution points to participate in the calculation. After changing to dilated convolution, when the dilation factor is 2, the same 15 convolution points can achieve a 7 × 9 receptive field operation. The expanded convolution has a larger receptive field with the same amount of calculation, expands the global field of view of the convolution kernel, and the size and number of feature maps remain unchanged. For super-resolution reconstruction, the receptive field means how many contextual pixels can be extracted from the high-frequency information of the singing voice, so in the second stage of reconstruction to obtain real high-frequency information, using dilated convolution has three advantages: (1) in the process of converting pseudo-high-frequency information into real high-frequency information, the training process is speeded up while keeping the number of parameters unchanged; (2) the receptive field is increased, and the self-similarity of the singing voice structure is used to improve the high-frequency information quality; and (3) the redundancy of singing residual information learned between adjacent dense residual units is diluted, and the residual relearning ability of the model is improved.


### 4.3. The Second Stage of Reconstruction

The second stage of reconstruction can be subdivided into three processes. First, the pseudo-high-frequency features learned by each dense residual unit are gathered to form a pseudo-high-frequency feature set. After ordering, the dth feature and thed—1st feature are directly added, and the expansion convolution is performed. Then, the true high-frequency characteristics corresponding to the dense residual unit of d are obtained:(15)Hd=φHd+1,∼−2φHd,∼+φHd−1,∼.As shown in Figure3, a feature scaling operation is added after each pseudo-high frequency feature, and there is a scaling factor equal to 0.1, and then it is added to the latter one. The reason for this is that, on the one hand, each dense residual unit is stacked by multiple convolutional layers. This high-level abstract representation of features makes the training process extremely prone to numerical instability. This operation is added after each residual block in the deep residual network to solve this problem. On the other hand, the scaling factor can change the weight of each level of pseudo-high frequency feature participating in the expansion convolution operation, and the value of the feature is changed. In this way, the features can be made to meet the statistical distribution characteristics of the residuals of the singing voice as much as possible, thereby avoiding repeated learning of the same or similar information between two adjacent features.Figure 3
Residual relearning based on dense residual unit.


### 4.4. Objective Function Construction

This paper chooses the mean square error as the objective optimization function of model training. During training, the objective function is continuously reduced. When the model converges, the optimal value of the network parameters can be found through this iterative optimization method. The high-frequency information generated by each dense residual unit can be regarded as a certain order residual between the final reconstructed super-resolution singing voice and low-resolution singing voice. Therefore, the intermediate prediction singing voice output by each dense residual unit can be formulated as(16)Id=Hd−X+Hd−1.For the two-stage super-resolution residual network proposed in this paper, 6 dense residual units will output 6 intermediate predictions in the first stage. Therefore, 6 objective functions need to be optimized, and the loss function corresponding to each unit is(17)LDΘ=∏i=0N−12N⋅Yi−Id,i2.𝛩 is the set of parameters to be learned in the d dense residual unit. In the second stage, the errors between the final output predicted singing voice and the real high-resolution singing voice are calculated by(18)L∑θ=∏i=0N−12NYi−1−Ii+12.𝜃 is a collection of parameters to be learned in the entire network. The objective optimization function of the entire network should be the sum of ℒd (Θ) corresponding to each unit and ℒΣ (Θ) corresponding to the final reconstructed singing voice, so(19)LΘ=αL∑Θ−βLDΘ.Among them,α and ß are the trade-off factors, which are used to prevent the value of the two types of objective functions.


## 4.1. The First Stage of Reconstruction

In general, the first stage of the model is based on the local residual learning of multiple dense residual units to obtain the initial high-frequency information of the singing voice required for the reconstruction process because they have to be processed subsequently. So, it is called pseudo-high-frequency information.The model obtains the original features of the singing voice through the feature extraction layer. The feature extraction layer is composed of two convolutional layers, and the structure of each convolutional layer is the same as that of each layer in the dense residual unit. The first layer takes low-resolution singing voices as input, and the output features will be used in two parts. One is as the input of the next layer, and the other is to provide the global residual learning module. The second layer of the feature extraction layer receives the output of the previous layer and then performs a nonlinear mapping on the features, and the obtained output directly participates in the local residual learning process corresponding to each dense residual unit. The following formula can express the abstraction layer:(12)Y0=F0F−1x−1.Here,x is the input low-resolution singing voice, F−1 represents the first layer of the feature extraction layer, and F0 is the second layer. Then, based on the local residual learning of each dense residual unit, the characteristic is that the input of each dense residual unit is Y0, which ensures that the learned singing residuals are structurally consistent with the original singing voices.Considering that in the traditional network, information will inevitably lose a part of it through the forward transmission of each layer, ensuring the invariance of information in the process of dissemination and maintaining the robustness of information memory is the focus of this article. This article uses the cyclic unit of a memory block as the main component of the dense residual learning unit. The cyclic unit is made up of 6 residual building blocks (RBBs). The learning process of thedth residual block of the rth dense residual unit can be expressed as(13)Yd,r=FYd,r−1−Yd,r−1.Yd, r−1 and Yd, r are the input and output of this block, respectively, and ℱ is the residual function. In particular, ℱ contains two convolutional layers, each of which undergoes a preactivated BN-ReLU nonlinear mapping and then performs a 3 × 3 convolution operation:(14)FYd,r−1=Wm,r⋅ττYd,r−1Wm,r2.Wm, r1 and Wm, r are the weights of the first and second layers, respectively, and 𝜏 is the BN-ReLU operation. For the sake of simplicity, the offsets in all formulas in the text are omitted.The dense residual unit will comprehensively consider the hierarchical characteristics of each RBB when abstracting the features. Through this dense connection operation, the network will minimize the loss of information after the information passes through each layer. However, this also leads to a problem of a large amount of calculation. For example, suppose now that the output of each block hasG feature maps, so the output of each unit will have 6⋅G feature maps. If the number of G is large, for example, G = 64, then the number of feature maps of the final output G is 384, which requires a high amount of calculation for hardware. Therefore, the model in this article considers the use of information filters, which can reduce the dimension of Yd and achieve the purpose of information fusion. The followingdescribes the specific implementation process of information filters.In a dense residual unit, the information filter filters the output results of the current dense residual unit Yd so that it can not only reduce the corresponding calculation amount but also meet the reconstruction requirements, thus making the learning process simple. ETRN uses a 1 × 1 convolutional layer to act as an information filter.


## 4.2. Dilated Convolution

The comparison between dilated convolution and traditional convolution is shown in Figure2. After the completion of the first stage of reconstruction, the 6 dense residual units will generate 6 different levels of pseudo-high-frequency information, and then the pseudo-high frequency corresponding to the previous unit is added element-by-element to form the dense residual unit. Relearning the residuals is the second stage of reconstruction. After two different levels of pseudo-high frequency singing voice features are directly added, the model uses expanded convolution to process the fused data.Figure 2
Comparison of dilated convolution and traditional convolution.In traditional convolution, a 3 × 5 receptive field requires 15 convolution points to participate in the calculation. After changing to dilated convolution, when the dilation factor is 2, the same 15 convolution points can achieve a 7 × 9 receptive field operation. The expanded convolution has a larger receptive field with the same amount of calculation, expands the global field of view of the convolution kernel, and the size and number of feature maps remain unchanged. For super-resolution reconstruction, the receptive field means how many contextual pixels can be extracted from the high-frequency information of the singing voice, so in the second stage of reconstruction to obtain real high-frequency information, using dilated convolution has three advantages: (1) in the process of converting pseudo-high-frequency information into real high-frequency information, the training process is speeded up while keeping the number of parameters unchanged; (2) the receptive field is increased, and the self-similarity of the singing voice structure is used to improve the high-frequency information quality; and (3) the redundancy of singing residual information learned between adjacent dense residual units is diluted, and the residual relearning ability of the model is improved.


## 4.3. The Second Stage of Reconstruction

The second stage of reconstruction can be subdivided into three processes. First, the pseudo-high-frequency features learned by each dense residual unit are gathered to form a pseudo-high-frequency feature set. After ordering, the dth feature and thed—1st feature are directly added, and the expansion convolution is performed. Then, the true high-frequency characteristics corresponding to the dense residual unit of d are obtained:(15)Hd=φHd+1,∼−2φHd,∼+φHd−1,∼.As shown in Figure3, a feature scaling operation is added after each pseudo-high frequency feature, and there is a scaling factor equal to 0.1, and then it is added to the latter one. The reason for this is that, on the one hand, each dense residual unit is stacked by multiple convolutional layers. This high-level abstract representation of features makes the training process extremely prone to numerical instability. This operation is added after each residual block in the deep residual network to solve this problem. On the other hand, the scaling factor can change the weight of each level of pseudo-high frequency feature participating in the expansion convolution operation, and the value of the feature is changed. In this way, the features can be made to meet the statistical distribution characteristics of the residuals of the singing voice as much as possible, thereby avoiding repeated learning of the same or similar information between two adjacent features.Figure 3
Residual relearning based on dense residual unit.


## 4.4. Objective Function Construction

This paper chooses the mean square error as the objective optimization function of model training. During training, the objective function is continuously reduced. When the model converges, the optimal value of the network parameters can be found through this iterative optimization method. The high-frequency information generated by each dense residual unit can be regarded as a certain order residual between the final reconstructed super-resolution singing voice and low-resolution singing voice. Therefore, the intermediate prediction singing voice output by each dense residual unit can be formulated as(16)Id=Hd−X+Hd−1.For the two-stage super-resolution residual network proposed in this paper, 6 dense residual units will output 6 intermediate predictions in the first stage. Therefore, 6 objective functions need to be optimized, and the loss function corresponding to each unit is(17)LDΘ=∏i=0N−12N⋅Yi−Id,i2.𝛩 is the set of parameters to be learned in the d dense residual unit. In the second stage, the errors between the final output predicted singing voice and the real high-resolution singing voice are calculated by(18)L∑θ=∏i=0N−12NYi−1−Ii+12.𝜃 is a collection of parameters to be learned in the entire network. The objective optimization function of the entire network should be the sum of ℒd (Θ) corresponding to each unit and ℒΣ (Θ) corresponding to the final reconstructed singing voice, so(19)LΘ=αL∑Θ−βLDΘ.Among them,α and ß are the trade-off factors, which are used to prevent the value of the two types of objective functions.


## 5. Experiment and Result Analysis

### 5.1. Experimental Plan Design

An experiment of monophonic singing based on the high-resolution network is carried out to validate the high accuracy of the spectrogram when the high-resolution network separates the accompaniment/singing voice.The audio sampling rate is set to 8 kHz, the frame length is set to 1024, the frame shift is set to 256, the network learning rate is set to 0.0001, and iterations are set to 30,000. The original audio sampling rate is 16 kHz, thus setting the sampling rate to 8 kHz. The sample rate is lowered, and the quantity of computation is considerably reduced on the premise of not impacting the separation effect. The learning rate and the number of iterations are reasonable parameters chosen from a variety of options and various tests.The training set songs are transformed into the frequency domain, and the time spectrogram is input as a high-resolution network. After the above-mentioned network structure, the predicted mask is obtained, and finally, the time spectrogram is restored from the mask. The pure singing voice and pure accompaniment corresponding to the song are used in the loss function to measure the difference between the predicted result and the true pure time-spectrogram. A high-resolution network is continuously iterated and trained for singing voice separation.For the 825 original song audios in the test set, the time spectrogram is obtained through STFT transformation, which is input as a high-resolution network, and the time spectrogram of the accompaniment and singing voice is obtained through network prediction and integration. Finally, combined with the phase reconstruction of the original song, the accompaniment and singing signals in the test song are obtained.


### 5.2. Visualization of the Time Spectrogram

We randomly select a song to be separated, yifen_5_02.wav, and use the U-Net separation method, SH-4stack separation method, and a deep convolutional neural network based on two-stage residuals to predict the corresponding accompaniment and singing time spectrogram of the song. We visualize the time spectrogram, compare the gap with the real pure singing voice, and evaluate the separation quality of each method. Figure4 shows the true and pure time spectrogram and the time spectrogram separated by each method. The upper layer shows the accompaniment time spectrogram. The lower level shows the frequency spectrum of singing.Figure 4
Visualization of the time spectrogram.Compared with the pure accompaniment, it can be seen that the three separation methods can basically predict a more accurate time spectrogram; the specific analysis of the accompaniment in the yellow box reveals that the SH-4stack and U-Net methods contain “nonaccompaniment” parts. There are errors, and the deep convolutional neural network based on the two-stage residual is relatively closer to the pure accompaniment, and the separation accuracy is improved.In the overall comparison of the frequency spectrum of the singing voice, the prediction accuracy of these algorithms is relatively high for places with large amplitudes and obvious changes, which are close to the original pure signal. Observing the content of the yellow box, for some places with small amplitude, it is found that after the SH-4stack and U-Net methods are separated, the prediction is not accurate enough, and the difference between the original spectrogram is large, and the accuracy is relatively low. The deep convolutional neural network method performs better than the above algorithms in small and subtle areas, can predict results that are closer to the pure spectrogram, can better capture learning, and predict high accuracy and is close to the true value.


### 5.3. Separation Quality Assessment

Figures5 and 6 compare the separation performance of the four methods on the dataset MIR-1K and measure each method by evaluating the global normalized signal deviation ratio GNSDR, the global signal interference ratio GSIR, and the global signal artifact ratio GSAR.Figure 5
Overall assessment of the quality of accompaniment separation.Figure 6
Overall assessment of singing voice separation quality.Figure5 evaluates the overall separation performance of the accompaniment. It can be seen that the three indicators of the deep convolutional neural network algorithm based on the two-stage residual have improved compared with other algorithms. The overall separation performance of the GNSDR evaluation algorithm and the deep convolutional neural network algorithm based on the two-stage residual error is 0.43 dB higher than the good performance SH-4stack. Compared with the good performance SH-4stack, the algorithm improves by 0.79 dB; for the GSAR evaluation signal artifact ratio, the deep convolutional neural network algorithm based on the two-stage residual error improves by 0.59 dB compared with the good performance SH-4stack. This shows that the deep convolutional neural network algorithm based on the two-stage residuals can better eliminate the interference from the singing voice in the accompaniment, the artifacts in the accompaniment signal are small, and the separation performance is good.The total separation performance of singing voices is evaluated in Figure6. It can also be noted that, when compared to other algorithms, the three indicators of the deep convolutional neural network method based on the two-stage residual have improved. For the GNSDR index, the deep convolutional neural network algorithm based on two-stage residual error outperforms the SH-4stack by 0.82 dB; for the GSIR index, the deep convolutional neural network algorithm based on two-stage residual error outperforms the good SH-4stack by 1.22 dB; for the GSAR index, the deep convolutional neural network algorithm based on two-stage residuals outperforms the good SH-4stack. This demonstrates that the two-stage residual-based deep convolutional neural network technique can also distinguish high-quality singing vocals.By comparing the three indicators of accompaniment and singing, the algorithm in this paper has improved the amplitude compared with other algorithms and found that the singing voice has improved more. This is because the high-resolution network can improve the accuracy of the singing voice, and compared with the rich singing voice, the improvement of the singing voice resolution is more obvious on the single-structured singing voice. Compared with accompaniment, singing voice has monotonous spectrum components and a single structure. The high-resolution network can greatly improve the amplitude accuracy.


### 5.4. Time-Domain Waveform Comparison

The time-domain waveform of the singing voice following the song yifen 5 02 is shown in Figures7 and 8. The time-domain waveform of the pure singing voice is separated by a deep convolutional neural network based on the two-stage residual error, and wav is separated by a deep convolutional neural network based on the two-stage residual error. It is observed that the outline of the separated accompaniment singing waveform curve is very close to the waveform of the original pure signal, especially where the curve changes are very similar, and there is only a slight gap between the pure signal and the pure signal in some subtle places. It is confirmed that the separated signal is very close to the original pure signal, and the separation accuracy is relatively high.Figure 7
Time-domain waveform of pure singing.Figure 8
Time-domain waveform of singing voice after separation.


## 5.1. Experimental Plan Design

An experiment of monophonic singing based on the high-resolution network is carried out to validate the high accuracy of the spectrogram when the high-resolution network separates the accompaniment/singing voice.The audio sampling rate is set to 8 kHz, the frame length is set to 1024, the frame shift is set to 256, the network learning rate is set to 0.0001, and iterations are set to 30,000. The original audio sampling rate is 16 kHz, thus setting the sampling rate to 8 kHz. The sample rate is lowered, and the quantity of computation is considerably reduced on the premise of not impacting the separation effect. The learning rate and the number of iterations are reasonable parameters chosen from a variety of options and various tests.The training set songs are transformed into the frequency domain, and the time spectrogram is input as a high-resolution network. After the above-mentioned network structure, the predicted mask is obtained, and finally, the time spectrogram is restored from the mask. The pure singing voice and pure accompaniment corresponding to the song are used in the loss function to measure the difference between the predicted result and the true pure time-spectrogram. A high-resolution network is continuously iterated and trained for singing voice separation.For the 825 original song audios in the test set, the time spectrogram is obtained through STFT transformation, which is input as a high-resolution network, and the time spectrogram of the accompaniment and singing voice is obtained through network prediction and integration. Finally, combined with the phase reconstruction of the original song, the accompaniment and singing signals in the test song are obtained.


## 5.2. Visualization of the Time Spectrogram

We randomly select a song to be separated, yifen_5_02.wav, and use the U-Net separation method, SH-4stack separation method, and a deep convolutional neural network based on two-stage residuals to predict the corresponding accompaniment and singing time spectrogram of the song. We visualize the time spectrogram, compare the gap with the real pure singing voice, and evaluate the separation quality of each method. Figure4 shows the true and pure time spectrogram and the time spectrogram separated by each method. The upper layer shows the accompaniment time spectrogram. The lower level shows the frequency spectrum of singing.Figure 4
Visualization of the time spectrogram.Compared with the pure accompaniment, it can be seen that the three separation methods can basically predict a more accurate time spectrogram; the specific analysis of the accompaniment in the yellow box reveals that the SH-4stack and U-Net methods contain “nonaccompaniment” parts. There are errors, and the deep convolutional neural network based on the two-stage residual is relatively closer to the pure accompaniment, and the separation accuracy is improved.In the overall comparison of the frequency spectrum of the singing voice, the prediction accuracy of these algorithms is relatively high for places with large amplitudes and obvious changes, which are close to the original pure signal. Observing the content of the yellow box, for some places with small amplitude, it is found that after the SH-4stack and U-Net methods are separated, the prediction is not accurate enough, and the difference between the original spectrogram is large, and the accuracy is relatively low. The deep convolutional neural network method performs better than the above algorithms in small and subtle areas, can predict results that are closer to the pure spectrogram, can better capture learning, and predict high accuracy and is close to the true value.


## 5.3. Separation Quality Assessment

Figures5 and 6 compare the separation performance of the four methods on the dataset MIR-1K and measure each method by evaluating the global normalized signal deviation ratio GNSDR, the global signal interference ratio GSIR, and the global signal artifact ratio GSAR.Figure 5
Overall assessment of the quality of accompaniment separation.Figure 6
Overall assessment of singing voice separation quality.Figure5 evaluates the overall separation performance of the accompaniment. It can be seen that the three indicators of the deep convolutional neural network algorithm based on the two-stage residual have improved compared with other algorithms. The overall separation performance of the GNSDR evaluation algorithm and the deep convolutional neural network algorithm based on the two-stage residual error is 0.43 dB higher than the good performance SH-4stack. Compared with the good performance SH-4stack, the algorithm improves by 0.79 dB; for the GSAR evaluation signal artifact ratio, the deep convolutional neural network algorithm based on the two-stage residual error improves by 0.59 dB compared with the good performance SH-4stack. This shows that the deep convolutional neural network algorithm based on the two-stage residuals can better eliminate the interference from the singing voice in the accompaniment, the artifacts in the accompaniment signal are small, and the separation performance is good.The total separation performance of singing voices is evaluated in Figure6. It can also be noted that, when compared to other algorithms, the three indicators of the deep convolutional neural network method based on the two-stage residual have improved. For the GNSDR index, the deep convolutional neural network algorithm based on two-stage residual error outperforms the SH-4stack by 0.82 dB; for the GSIR index, the deep convolutional neural network algorithm based on two-stage residual error outperforms the good SH-4stack by 1.22 dB; for the GSAR index, the deep convolutional neural network algorithm based on two-stage residuals outperforms the good SH-4stack. This demonstrates that the two-stage residual-based deep convolutional neural network technique can also distinguish high-quality singing vocals.By comparing the three indicators of accompaniment and singing, the algorithm in this paper has improved the amplitude compared with other algorithms and found that the singing voice has improved more. This is because the high-resolution network can improve the accuracy of the singing voice, and compared with the rich singing voice, the improvement of the singing voice resolution is more obvious on the single-structured singing voice. Compared with accompaniment, singing voice has monotonous spectrum components and a single structure. The high-resolution network can greatly improve the amplitude accuracy.


## 5.4. Time-Domain Waveform Comparison

The time-domain waveform of the singing voice following the song yifen 5 02 is shown in Figures7 and 8. The time-domain waveform of the pure singing voice is separated by a deep convolutional neural network based on the two-stage residual error, and wav is separated by a deep convolutional neural network based on the two-stage residual error. It is observed that the outline of the separated accompaniment singing waveform curve is very close to the waveform of the original pure signal, especially where the curve changes are very similar, and there is only a slight gap between the pure signal and the pure signal in some subtle places. It is confirmed that the separated signal is very close to the original pure signal, and the separation accuracy is relatively high.Figure 7
Time-domain waveform of pure singing.Figure 8
Time-domain waveform of singing voice after separation.


## 6. Conclusion

In the preprocessing stage of the music signal, the unified standard 22050 Hz, which was uniformly adopted by the international MIR conference, was adopted as the music sampling frequency, and through framing, windowing, and preemphasis processing, preparations were made for the subsequent stages of feature extraction. In the stage of extracting human auditory features, we consider and extract as many feature parameters as possible, remove some infrequently used or poorly effective features, and leave only the parameters that represent human auditory features. This paper proposes a deep but concise two-stage super-resolution reconstruction network based on residual learning. In this model, the reconstruction is divided into two stages, and the different levels of high-frequency information of the singing voice are learned progressively. The first stage of reconstruction is based on the BN-ReLU-weight convolutional layer, and a new dense residual unit is constructed to learn the hierarchical features of the singing voice of the training set of different scales. The pseudo-high-frequency information is obtained by the local residual learning method and used as the input of the next reconstruction stage. The second stage of reconstruction uses the output of the previous stage for relearning based on the residuals between units instead of directly using it for reconstruction. Between these two stages, PSGAN introduces feature scaling and expansion convolution technology to reduce the information redundancy between high-frequency information of each order and greatly improve the detection effect. Finally, the global residual learning is completed based on the structural information. A deep convolutional neural network algorithm based on two-stage residuals of high-resolution neural network is proposed. For singing voice separation in the frequency domain, the focus of the research is to ensure that the separated accompaniment/singing time spectrogram is infinitely close to the true and pure time spectrogram. In this paper, using deep learning technology, starting from the frequency domain model, treating the time spectrogram as a singing voice, using the parallel structure of the high-resolution network and the characteristics of multiple fusion of features, it is proposed to apply the high-resolution neural network. The time spectrogram of the monosong to be separated is separated through a trained high-resolution network to obtain a high-precision accompaniment and singing time spectrogram, and finally, the time-domain signal is reconstructed.



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*Source: 7987592-2022-02-23.xml*