# Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Authors: Lu Zang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

With the continuous development of digital technology, music, as an important form of media, and its digital audio technology is also constantly developing, forcing the traditional music industry to start the road of digital transformation. What kind of method can be used to automatically retrieve music information effectively and quickly in vocal singing has become one of the current research topics that has attracted much attention. Aiming at this problem, it is of great research significance for the field of timbre feature recognition. With the in-depth research on timbre feature recognition, the research on timbre feature extraction by machine learning in vocal singing has also been gradually carried out, and its performance advantages are of great significance to solve the problem of automatic retrieval of music information. This paper aims to study the application of feature extraction algorithm based on machine learning in timbre feature extraction in vocal singing. Through the analysis and research of machine learning and feature extraction methods, it can be applied to the construction of timbre feature extraction algorithms to solve the problem of automatic retrieval of music information. This paper analyzed vocal singing, machine learning, and feature extraction, experimentally analyzed the performance of the method, and used related theoretical formulas to explain. The results have showed that the method for timbre feature extraction in the vocal singing environment was more accurate than the traditional method, the difference between the two was 24.27%, and the proportion of satisfied users was increased by 33%. It can be seen that this method can meet the needs of users for timbre feature extraction in the use of music software, and the work efficiency and user satisfaction are greatly improved.


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## Body

## 1. Introduction

In the current era of digital information explosion, the digital transformation of music is also advancing. Ordinary timbre feature extraction methods have been unable to meet people’s increasing requirements in terms of speed and accuracy for automatic retrieval of music information in vocal singing under a variety of complex elements. Machine learning is a new computer discipline that can be used to solve complex and computationally expensive problems. Due to its advantages in performance, it has been applied to various fields to successfully solve various technical problems. It is an interdisciplinary subject of how computers simulate and implement human learning behaviors to continuously improve their performance. For the complex musical elements in vocal singing, it has far-reaching significance for how to quickly and efficiently perform automatic retrieval of music information when musical tones and speech coexist. The extraction technology of timbre features has a good effect on the problem of automatic retrieval of music information to be solved and has less restrictions. Therefore, it is used as a common method. Feature extraction is a method of transforming the group measurements of a pattern to highlight the representative features of the pattern. In recent years, scholars have used feature extraction for automatic retrieval of music information, but there are relatively few applications and researches on feature extraction machine learning in vocal singing. Therefore, it is of great significance to apply machine learning to the study of timbre feature extraction methods in vocal singing.At present, the upsurge of digital audio continues to rise, and more and more scholars have explored the extraction methods of timbre features in music. In order to improve the extraction efficiency of timbre features, Wang performed feature extraction by using local degradation features and global statistical features [1]. To extract emotional features in music, Chin proposed a new system for identifying emotional content in music [2]. To get better spectral resolution at low frequencies, Birajdar proposed a new feature extraction method for speech/music classification based on generalized Gaussian distributed descriptors extracted from IIR-CQT spectrogram representation [3]. Zhang presented a new wavelet-based method for musical instrument classification, which represented local and global information by computing wavelet coefficients of different frequency subbands with different resolutions [4]. In order to improve the processing speed of music feature extraction, Silva put forward an efficient SiMPle-Fast method for accurate calculation of SiMPle, which was an order of magnitude faster than SiMPle [5]. These methods advance research in this field. However, the accuracy of the timbre feature extraction method used in vocal singing is not high.Machine learning can be used for timbre feature extraction in vocal singing, and has a good performance in the extraction accuracy of feature parameters. Panella designed a machine learning algorithm to recognize gestures by using Hu image moments with low computational cost [6]. Jenke conducted research on emotion recognition by performing feature selection comparative experiments on emotion recorded datasets through machine learning [7]. Khan proposed a real-time detection method of vibration features based on machine learning model suitable for static environment [8]. For better dynamic feature analysis, Zhao proposed a hybrid grammar (H-gram) feature extraction method with continuous overlapping subsequence cross-entropy, which realized semantic segmentation of a series of API calls or instructions [9]. These methods improve the accuracy of feature extraction to a certain extent, but the methods themselves are too complicated.In order to solve the problem of low accuracy of timbre feature extraction in vocal singing, this paper uses machine learning to analyze timbre feature extraction, and simulates timbre extraction in vocal singing to achieve the effect of improving feature extraction efficiency. The innovation of this paper is that machine learning is used to analyze how machine learning, feature extraction technology, and vocal singing play a role in the study of timbre feature extraction methods in vocal singing based on machine learning. The proposed feature extraction method is expounded, and it is found through experiments that the method has better performance, stronger practicability, and greatly improves the efficiency of timbre feature extraction. This paper mainly introduces the research background of the research problem of timbre feature extraction in vocal singing based on machine learning, and leads to the problems to be solved to illustrate the purpose and significance of this research. Then, it makes a general analysis of the research status in the field of timbre feature extraction and the application field of machine learning, and explains the content and innovation of this paper; it also describes the organization structure and method of the full text of this paper, and analyzes and describes the related methods of vocal singing, machine learning, and feature extraction; then, the data source of this paper is explained in detail. These data are the data of the instrument monophonic signal from FL Studio12; after arranging the data, after analyzing the result data, a conclusion is drawn; finally, the full text is summarized.


## 2. Method of Timbre Feature Extraction Methods in Vocal Singing

The research on automatic retrieval of music information in different scenarios continues to deepen, and the defects and deficiencies of using traditional methods for retrieval in vocal singing become increasingly prominent. For example, the retrieval response time is long and the accuracy is not high. Therefore, it is very important to use machine learning to improve the accuracy of timbre sign extraction in vocal singing [10]. The music scene is shown in Figure 1:Figure 1
Different music scenes. (a) Musical instrument performance. (b) Vocal singing.
(a)(b)Through the investigation, it is found that the current research on timbre feature extraction in vocal music singing is not complete enough; it mainly focuses on the feature extraction of musical instrument timbre, and there is less research on timbre feature extraction in the complex environment of vocal singing. So this paper proposes a research on the timbre feature extraction method of machine learning in vocal music singing [11, 12]. This paper analyzes machine learning and feature extraction methods and vocal singing, and applies the timbre feature extraction method to timbre extraction in vocal singing. The analysis shows that the feature extraction method based on machine learning has better feature extraction effect than other methods.Searches for music information in the past were basically traditional text searches. Text information corresponding to music files can only be obtained by manual annotation. Due to the large number of multimedia files, the labor and time cost of this method is high. At the same time, the complete information of music cannot be fully represented by text, especially information such as pitch, timbre, and melody rhythm that reflect the characteristics of the music signal itself [13]. The characteristics of the music signal are shown in Figure 2:Figure 2
Music signal characteristics.The loss of this information will seriously affect the accuracy of music search results, thereby reducing search efficiency. Even if someone proposes automatic musical instrument timbre recognition, the feature extraction of musical instrument timbre can achieve better accuracy in the identification of pure music environment. However, the extraction accuracy of simple musical instrument timbre features in vocal singing is not satisfactory [14]. Vocal singing is divided into various types of vocal singing classification, as shown in Figure 3:Figure 3
Types of vocal singing.In vocal singing, the music environment is very complex, and it is relatively difficult to extract all timbre features [15]. Take the musical excerpt “The Phantom of the Opera” as an example, as shown in Figure 4:Figure 4
The musical “The Phantom of the Opera.”As shown in Figure4: In the musical performance environment, there are accompaniment music, song arias, audience, and ambient sound at the same time. The musical of the same name, The Phantom of the Opera, is a famous song among the famous arias in the play, and it is also the theme song. It is a grand and exciting song and is called the highest peak of the musical. The range to the highest E, the excellent duet transitions, and the powerful drama make this song incomparable to other musical theatre choices throughout the musical’s history. The protagonist male and female duets and chorus are the main singing, mainly accompanied by pipe organs and stringed instruments. This huge sound quality effect and strong sense of theme create a sense of tension and excitement [16]. The extraction of timbre features in such complex musical elements requires a very high degree of discrimination for different timbres. Therefore, based on machine learning, this paper proposes to detect the harmonic structure of speech and musical tones through algorithm fusion to improve the efficiency of feature extraction in vocal singing. The related methods are analyzed in the next part.


### 2.1. Feature Extraction in Vocal Singing

Autocorrelation coefficients and zero-crossing ratios are time-domain features computed directly from the audio signal [17]. Time domain information uses time as a variable to describe the waveform of the signal. The spectral distribution in the time domain of the signal is represented by the autocorrelation coefficient, which can be well described to provide classification. Its calculation is expressed as formula (1):(1)az=1a0∑n=0Cn−z−1xnxn+z,Cn is the window length; z is the time lag.The zero crossing ratio is the number of times the signal value crosses the zero axis. Generally speaking, the sound value of the law is small, and the noise value is large. This is calculated by subtracting the local offset of the signal per frame and normalizing the zero crossing rate value per frame according to the window length.The logarithmic onset time is used to predict the start and end times of music, and its definition is shown in formula (2):(2)L=log10send−sst,send is the end time of the tone; sst is the start time of the tone.The time center of gravity is the moment at which the center of mass of the signal energy envelope is located. Percussion and sustained sound are differentiated by the time center of gravity, as shown in formula (3):(3)tc=∑n=n1n=nersn∗sn∑nrsn,n1 is the first value of n; ne is the last value of n.Amplitude envelopes are macroscopic detections of waveforms. The common method for calculating the amplitude envelope is the RMS algorithm, and its calculation process is shown in formula (4):(4)R=1C∑n=0Ck2n.The RMS value approximates the sensitivity of the human auditory system to changes in audio signal strength. This paper obtains the mean and variance of the RMS energy envelope for all frames.The spectral energy is the sum of the temporal amplitudes after the Fourier transform. The experiment extracts features from the STFT energy spectrum and STFT power spectrum of all frames of the signal, and calculates their mean and variance, as shown in formula (5):(5)ESsm=∑kbk2sm,sm is the tone signal time; bk2 is the amplitude.Formants are not only determinants of sound quality but also reflect the physical properties of the resonator. The cepstral coefficient can represent the resonance peak, and the definition of the cepstral coefficient including the signal is shown in formula (6):(6)vn=J−1logJxn,xn is the semaphore; J is the discrete Fourier transform. However, the computational efficiency of this method is low, and it is not really used.The linear prediction cepstral coefficient can play a better role in the actual timbre recognition. The main idea of linear prediction is to use a linear combination of past samples to represent the current sample.The basic principle of linear prediction is to represent signals analyzed using a model, that is, treat the signal as the output of a particular model so that the model parameters can be used to describe the signal.in is the signal output; on is the signal output. When the output is a definite signal, it indicates that the input signal is a unit impulse sequence; when the output is a random signal, the input can be a white noise sequence.Fe is the transfer function, which can be represented by a rational fraction, as shown in formula (7):(7)fe=GMeNe.The specific meaning is shown in formula (8):(8)Ne=1−∑k=1pnke−k,Me=1+∑k=1qmke−kFe=∑k=1pfke−k.,G is the gain factor;nk,mk is the model parameter; p,q is the model order.The system function of the synthesis filter can be obtained by linear prediction analysis, as shown in formula (9):(9)Fe=11−∑i=1pmie−1.The impulse response isfn, and the calculation formula of the cepstral coefficient can be obtained, as shown in formulas (10) and (11):(10)f^(n)=mn+∑i=1n−11−inmif^(n−i)1<n<p,(11)f^(n)=∑i=1n−11−inmif^n−in>p,mi is the prediction coefficient. The cepstrum can be directly obtained by using the prediction coefficients through these formulas, and some troubles of processing in the same state are avoided, such as in the general homomorphism processing complex logarithm and other problems.


### 2.2. Timbre Feature Extraction Method Based on Machine Learning

Supervised learning is often used for classification and recognition of timbre features. Supervised learning uses a training dataset to learn a model, and uses the model to predict a test sample set. Supervised learning is divided into two processes: learning and prediction, which are completed by the learning system and the prediction system, respectively [18], as shown in Figure 5:Figure 5
Schematic diagram of supervised learning process.In timbre recognition, the training of timbre models and the identification of timbres are based on selected timbre-related characteristic parameters. In order to make the extracted features more effective, the musical tone signal is firstly analyzed and processed. Music signal preprocessing is a very important stage in music recognition and classification.Removal of silent segments: In this paper, a dual-gate endpoint detection method based on short-term energy is used to remove silent segments. Firstly, the signal is divided into frames, the short-term average energy is obtained, the frame-by-frame comparison is performed, and the judgment is made according to the threshold. A rough judgment will be based on a selected higher threshold in the short-term energy envelope of speech [19]. If the threshold is higher, it must be a sound, and the start and end of the music must be placed outside the time points corresponding to the intersection of the threshold and the energy envelope. The lower threshold of the average energy is determined, and the search is performed left and right from the previous intersection to find two points where the short-term energy intersects the threshold. This is the start and end position of the piece of music as determined by double precision. If it is considered that there may be a minimum length between musical signal notes to indicate a pause, the end of the musical segment can only be determined after the minimum length is less than the threshold. This is actually the same as extending the tail length.Preemphasis: Usually, a first-order digital filter is used to preemphasize before the feature parameters are extracted. This is to improve the high frequency resolution of the music signal for overall analysis. The filter transfer function is shown in formula (12):(12)fa=1−βa−1,β is the preemphasis factor. Its value is generally a decimal less than 1, and the value in this paper is 0.95. Through preemphasis processing, the input audio signal is transformed, as shown in formula (13):(13)yn=yn+1−βyn,yn is the original signal of the input audio signal.Framing windowing: Like speech signals, music signals can be viewed as relatively short-term stationary. The feature extraction of music signal is based on a steady state signal. Therefore, frame segmentation is usually required before extracting features of music signals [20]. The signal must be segmented into small segments of the signal with stable statistical characteristics. Frames are required for each segment of the signal. Due to the relatively slow time transition, the music signal may be slightly longer in each frame. To avoid losing information, frames must overlap by 1/3 to 1/2 frame between two frames, which is called frame shifting. The formula for calculating the number of frames is shown in formula (14):(14)Z=Z1−Z0Z2−Z0,Z is the number of frames; Z0 is the frame shift; Z1 is the total length of the signal; Z2 is the frame length.After segmenting all music clips into frames, it is also necessary to perform window operations on the segmented frames to improve the continuity between frames, reduce edge effects, and reduce spectral leakage [21].One of the commonly used window functions is the rectangular window, which is defined as formula (15):(15)Cn=1,0≤n≤u,0,other.The Hanning window is defined as formula (16):(16)Cn=0.51−cos2πnu−1,0≤n≤u,0,other.The definition of Hamming window is shown in formula (17):(17)Cn=0.54−0.46cos2πnu−1,0≤n≤u,0,other.u is the length of the window function, and they all have low-pass properties.Through the selection of timbre-related features in vocal singing, the input music signal is preprocessed, preemphasized, silenced segments removed, framed and windowed, and then features are extracted based on machine learning algorithms. In this way, the timbre feature extraction in vocal music can achieve better results [22].


## 2.1. Feature Extraction in Vocal Singing

Autocorrelation coefficients and zero-crossing ratios are time-domain features computed directly from the audio signal [17]. Time domain information uses time as a variable to describe the waveform of the signal. The spectral distribution in the time domain of the signal is represented by the autocorrelation coefficient, which can be well described to provide classification. Its calculation is expressed as formula (1):(1)az=1a0∑n=0Cn−z−1xnxn+z,Cn is the window length; z is the time lag.The zero crossing ratio is the number of times the signal value crosses the zero axis. Generally speaking, the sound value of the law is small, and the noise value is large. This is calculated by subtracting the local offset of the signal per frame and normalizing the zero crossing rate value per frame according to the window length.The logarithmic onset time is used to predict the start and end times of music, and its definition is shown in formula (2):(2)L=log10send−sst,send is the end time of the tone; sst is the start time of the tone.The time center of gravity is the moment at which the center of mass of the signal energy envelope is located. Percussion and sustained sound are differentiated by the time center of gravity, as shown in formula (3):(3)tc=∑n=n1n=nersn∗sn∑nrsn,n1 is the first value of n; ne is the last value of n.Amplitude envelopes are macroscopic detections of waveforms. The common method for calculating the amplitude envelope is the RMS algorithm, and its calculation process is shown in formula (4):(4)R=1C∑n=0Ck2n.The RMS value approximates the sensitivity of the human auditory system to changes in audio signal strength. This paper obtains the mean and variance of the RMS energy envelope for all frames.The spectral energy is the sum of the temporal amplitudes after the Fourier transform. The experiment extracts features from the STFT energy spectrum and STFT power spectrum of all frames of the signal, and calculates their mean and variance, as shown in formula (5):(5)ESsm=∑kbk2sm,sm is the tone signal time; bk2 is the amplitude.Formants are not only determinants of sound quality but also reflect the physical properties of the resonator. The cepstral coefficient can represent the resonance peak, and the definition of the cepstral coefficient including the signal is shown in formula (6):(6)vn=J−1logJxn,xn is the semaphore; J is the discrete Fourier transform. However, the computational efficiency of this method is low, and it is not really used.The linear prediction cepstral coefficient can play a better role in the actual timbre recognition. The main idea of linear prediction is to use a linear combination of past samples to represent the current sample.The basic principle of linear prediction is to represent signals analyzed using a model, that is, treat the signal as the output of a particular model so that the model parameters can be used to describe the signal.in is the signal output; on is the signal output. When the output is a definite signal, it indicates that the input signal is a unit impulse sequence; when the output is a random signal, the input can be a white noise sequence.Fe is the transfer function, which can be represented by a rational fraction, as shown in formula (7):(7)fe=GMeNe.The specific meaning is shown in formula (8):(8)Ne=1−∑k=1pnke−k,Me=1+∑k=1qmke−kFe=∑k=1pfke−k.,G is the gain factor;nk,mk is the model parameter; p,q is the model order.The system function of the synthesis filter can be obtained by linear prediction analysis, as shown in formula (9):(9)Fe=11−∑i=1pmie−1.The impulse response isfn, and the calculation formula of the cepstral coefficient can be obtained, as shown in formulas (10) and (11):(10)f^(n)=mn+∑i=1n−11−inmif^(n−i)1<n<p,(11)f^(n)=∑i=1n−11−inmif^n−in>p,mi is the prediction coefficient. The cepstrum can be directly obtained by using the prediction coefficients through these formulas, and some troubles of processing in the same state are avoided, such as in the general homomorphism processing complex logarithm and other problems.


## 2.2. Timbre Feature Extraction Method Based on Machine Learning

Supervised learning is often used for classification and recognition of timbre features. Supervised learning uses a training dataset to learn a model, and uses the model to predict a test sample set. Supervised learning is divided into two processes: learning and prediction, which are completed by the learning system and the prediction system, respectively [18], as shown in Figure 5:Figure 5
Schematic diagram of supervised learning process.In timbre recognition, the training of timbre models and the identification of timbres are based on selected timbre-related characteristic parameters. In order to make the extracted features more effective, the musical tone signal is firstly analyzed and processed. Music signal preprocessing is a very important stage in music recognition and classification.Removal of silent segments: In this paper, a dual-gate endpoint detection method based on short-term energy is used to remove silent segments. Firstly, the signal is divided into frames, the short-term average energy is obtained, the frame-by-frame comparison is performed, and the judgment is made according to the threshold. A rough judgment will be based on a selected higher threshold in the short-term energy envelope of speech [19]. If the threshold is higher, it must be a sound, and the start and end of the music must be placed outside the time points corresponding to the intersection of the threshold and the energy envelope. The lower threshold of the average energy is determined, and the search is performed left and right from the previous intersection to find two points where the short-term energy intersects the threshold. This is the start and end position of the piece of music as determined by double precision. If it is considered that there may be a minimum length between musical signal notes to indicate a pause, the end of the musical segment can only be determined after the minimum length is less than the threshold. This is actually the same as extending the tail length.Preemphasis: Usually, a first-order digital filter is used to preemphasize before the feature parameters are extracted. This is to improve the high frequency resolution of the music signal for overall analysis. The filter transfer function is shown in formula (12):(12)fa=1−βa−1,β is the preemphasis factor. Its value is generally a decimal less than 1, and the value in this paper is 0.95. Through preemphasis processing, the input audio signal is transformed, as shown in formula (13):(13)yn=yn+1−βyn,yn is the original signal of the input audio signal.Framing windowing: Like speech signals, music signals can be viewed as relatively short-term stationary. The feature extraction of music signal is based on a steady state signal. Therefore, frame segmentation is usually required before extracting features of music signals [20]. The signal must be segmented into small segments of the signal with stable statistical characteristics. Frames are required for each segment of the signal. Due to the relatively slow time transition, the music signal may be slightly longer in each frame. To avoid losing information, frames must overlap by 1/3 to 1/2 frame between two frames, which is called frame shifting. The formula for calculating the number of frames is shown in formula (14):(14)Z=Z1−Z0Z2−Z0,Z is the number of frames; Z0 is the frame shift; Z1 is the total length of the signal; Z2 is the frame length.After segmenting all music clips into frames, it is also necessary to perform window operations on the segmented frames to improve the continuity between frames, reduce edge effects, and reduce spectral leakage [21].One of the commonly used window functions is the rectangular window, which is defined as formula (15):(15)Cn=1,0≤n≤u,0,other.The Hanning window is defined as formula (16):(16)Cn=0.51−cos2πnu−1,0≤n≤u,0,other.The definition of Hamming window is shown in formula (17):(17)Cn=0.54−0.46cos2πnu−1,0≤n≤u,0,other.u is the length of the window function, and they all have low-pass properties.Through the selection of timbre-related features in vocal singing, the input music signal is preprocessed, preemphasized, silenced segments removed, framed and windowed, and then features are extracted based on machine learning algorithms. In this way, the timbre feature extraction in vocal music can achieve better results [22].


## 3. Data Sources of Timbre Feature Extraction Methods in Vocal Singing

The data used for this algorithm test are the data of the instrument monophonic signal from FL Studio12. A total of 8 typical timbres from 4 categories of musical instruments are selected in the timbre library [23]. The specific content of the data is shown in Table 1:Table 1
Timbre samples selected for the experiment.
Instrument categoryNumber of samplesVoice numberKeyboard instrumentPiano001Celesta043PercussionTimpani028Marimba098Stringed instrumentViolin076Cello122Wind instrumentFlute101Trumpet016The timbre of the dataset consists of 4 categories, namely, keyboard instruments, percussion instruments, stringed instruments, and wind instruments. It is divided into 8 different monophonic sounds according to different categories [24].Then, the monophonic audio files with 7 notes in C4–B4 are, respectively, generated from 8 different typical timbres to form monophonic signal data sets of different pitches [25]. The pitch frequency table is shown in Table 2:Table 2
Pitch frequency.
01234C16.25432.54765.375130.247261.354D18.65736.54773.145146.215294.314E20.86441.35783.145167.432327.364F21.63443.86187.347179.658349.127G24.42748.98496.997196.385392.347A27.43955.451111.457221.321441.217B30.87661.247123.214246.254493.435


## 4. Results and Discussion of Timbre Feature Extraction Methods in Vocal Singing

### 4.1. Comparison of Harmonic Structures of Different Musical Instruments

In this paper, the harmonic structure of C4–B4 single tones of all different musical instruments selected in the experiment is extracted to form a harmonic structure diagram, and the specific results are shown in Figure6:Figure 6
Harmonic structure of different instruments. (a) Keyboards and wind instruments. (b) Pulled strings and percussion instruments.
(a)(b)As shown in Figure6: There are 8 small figures in the picture, including piano, cello, flute, trumpet, violin, cello, timpani, and marimba. Each small graph corresponds to the first 9 tones of the 7 mono wave coefficients. On the whole, it can be seen that the harmonic structures of different musical instruments are clearly distinguished, and the harmonic structures of the same type of musical instruments are relatively similar, which can be used as the basis for distinguishing the timbre of musical instruments [26].


### 4.2. Requirements for Timbre Feature Extraction in Vocal Singing

This paper collects and analyzes the data of 200 users who use music software on the current usage of the timbre extraction function and the usage in different environments through a questionnaire survey, and the specific results are shown in Figure7:Figure 7
Usage of timbre feature extraction function. (a) Degree of function usage. (b) Satisfaction level of use in different scenarios.
(a)(b)It can be seen from Figure7 that among the 200 users of music software, 18 people never use the tone extraction function, 36 people use this function occasionally, 87 people use this function frequently, and 59 people use this function every day. It can be seen that most music software users have a high degree of demand for this function, and the utilization rate accounts for 91%. On the one hand, this function is used to automatically identify and obtain music information, and on the other hand, it is convenient for digital editing in the later stage of music extraction. And 83 users think that they are most satisfied with the effect of this function in the music environment of solo instrument, accounting for 41.5%. However, users think that the use effect in vocal singing is not good, and only 18 users express satisfaction, accounting for 18%. It can be seen that users have a high demand for the timbre feature extraction function, and the current deficiencies of this function are manifested in poor use in the music environment of vocal singing, which provides a direction for improving this function.


### 4.3. Algorithm Training and Testing

In this paper, the harmonic structures of C4–B4 single tones of all different musical instruments generated by different kinds of musical instruments are used to extract the harmonic structure diagrams formed for the training and testing of the algorithm. In this paper, a total of 18 different musical instruments are selected, and a total of 126 sets of harmonic legends are generated. It is divided into training set and test set according to the ratio of 5:4, machine learning training and testing are carried out on the timbre feature extraction algorithm, the harmonic structure diagram is used as the output, and the output target is set as the name of the musical instrument that matches the timbre. The specific results are shown in Figure8:Figure 8
Algorithm training and testing results. (a) Training results. (b) Testing results.
(a)(b)As shown in Figure8: After 74 times of learning, the expected error is reached, and the output value is very close to the expected value. It can be seen that this model can be used to test the timbre extraction algorithm. From the test results shown in the figure, it can be seen that the test results for the extraction of timbre features of different types of musical instruments have a small error with the expected value and can achieve the expected accuracy, and have a high degree of recognition for the features of different timbres.


### 4.4. Comparison of Feature Extraction Effects in Different Environments before and after Optimization

In this paper, the timbre feature extraction method proposed in vocal singing is used in music software to test the timbre feature extraction function in real usage scenarios and compare it with the functional method before optimization. The timbre extraction accuracy and user satisfaction data results in different music environments are obtained. The specific results are shown in Figure9:Figure 9
Feature extraction effects in different environments before and after optimization. (a) Accuracy in different musical environments. (b) Changes in user satisfaction before and after optimization.
(a)(b)As shown in Figure9, from the results obtained by using different methods to extract timbre features in different environments, it can be seen that the overall timbre extraction accuracy of the optimized method has improved, reaching more than 80%. The extraction accuracy in the instrument solo environment is the highest, reaching 93.4%; the accuracy improvement in vocal singing is the most obvious, reaching 82.14%, which is 24.27% higher than that before optimization. This is because the optimized method uses machine learning to extract features for the harmonics of the timbre, which can better distinguish musical instruments from human voices in complex environments. From the user satisfaction before and after optimization, it can be seen that users’ satisfaction with the use of this function in the vocal singing environment has increased significantly, accounting for 42%, an increase of 33% compared to before optimization. It can be seen that this method has a significant effect on timbre extraction in vocal singing.


## 4.1. Comparison of Harmonic Structures of Different Musical Instruments

In this paper, the harmonic structure of C4–B4 single tones of all different musical instruments selected in the experiment is extracted to form a harmonic structure diagram, and the specific results are shown in Figure6:Figure 6
Harmonic structure of different instruments. (a) Keyboards and wind instruments. (b) Pulled strings and percussion instruments.
(a)(b)As shown in Figure6: There are 8 small figures in the picture, including piano, cello, flute, trumpet, violin, cello, timpani, and marimba. Each small graph corresponds to the first 9 tones of the 7 mono wave coefficients. On the whole, it can be seen that the harmonic structures of different musical instruments are clearly distinguished, and the harmonic structures of the same type of musical instruments are relatively similar, which can be used as the basis for distinguishing the timbre of musical instruments [26].


## 4.2. Requirements for Timbre Feature Extraction in Vocal Singing

This paper collects and analyzes the data of 200 users who use music software on the current usage of the timbre extraction function and the usage in different environments through a questionnaire survey, and the specific results are shown in Figure7:Figure 7
Usage of timbre feature extraction function. (a) Degree of function usage. (b) Satisfaction level of use in different scenarios.
(a)(b)It can be seen from Figure7 that among the 200 users of music software, 18 people never use the tone extraction function, 36 people use this function occasionally, 87 people use this function frequently, and 59 people use this function every day. It can be seen that most music software users have a high degree of demand for this function, and the utilization rate accounts for 91%. On the one hand, this function is used to automatically identify and obtain music information, and on the other hand, it is convenient for digital editing in the later stage of music extraction. And 83 users think that they are most satisfied with the effect of this function in the music environment of solo instrument, accounting for 41.5%. However, users think that the use effect in vocal singing is not good, and only 18 users express satisfaction, accounting for 18%. It can be seen that users have a high demand for the timbre feature extraction function, and the current deficiencies of this function are manifested in poor use in the music environment of vocal singing, which provides a direction for improving this function.


## 4.3. Algorithm Training and Testing

In this paper, the harmonic structures of C4–B4 single tones of all different musical instruments generated by different kinds of musical instruments are used to extract the harmonic structure diagrams formed for the training and testing of the algorithm. In this paper, a total of 18 different musical instruments are selected, and a total of 126 sets of harmonic legends are generated. It is divided into training set and test set according to the ratio of 5:4, machine learning training and testing are carried out on the timbre feature extraction algorithm, the harmonic structure diagram is used as the output, and the output target is set as the name of the musical instrument that matches the timbre. The specific results are shown in Figure8:Figure 8
Algorithm training and testing results. (a) Training results. (b) Testing results.
(a)(b)As shown in Figure8: After 74 times of learning, the expected error is reached, and the output value is very close to the expected value. It can be seen that this model can be used to test the timbre extraction algorithm. From the test results shown in the figure, it can be seen that the test results for the extraction of timbre features of different types of musical instruments have a small error with the expected value and can achieve the expected accuracy, and have a high degree of recognition for the features of different timbres.


## 4.4. Comparison of Feature Extraction Effects in Different Environments before and after Optimization

In this paper, the timbre feature extraction method proposed in vocal singing is used in music software to test the timbre feature extraction function in real usage scenarios and compare it with the functional method before optimization. The timbre extraction accuracy and user satisfaction data results in different music environments are obtained. The specific results are shown in Figure9:Figure 9
Feature extraction effects in different environments before and after optimization. (a) Accuracy in different musical environments. (b) Changes in user satisfaction before and after optimization.
(a)(b)As shown in Figure9, from the results obtained by using different methods to extract timbre features in different environments, it can be seen that the overall timbre extraction accuracy of the optimized method has improved, reaching more than 80%. The extraction accuracy in the instrument solo environment is the highest, reaching 93.4%; the accuracy improvement in vocal singing is the most obvious, reaching 82.14%, which is 24.27% higher than that before optimization. This is because the optimized method uses machine learning to extract features for the harmonics of the timbre, which can better distinguish musical instruments from human voices in complex environments. From the user satisfaction before and after optimization, it can be seen that users’ satisfaction with the use of this function in the vocal singing environment has increased significantly, accounting for 42%, an increase of 33% compared to before optimization. It can be seen that this method has a significant effect on timbre extraction in vocal singing.


## 5. Conclusions

The development of science and technology has changed the way people experience music and art, and people have higher and higher requirements for timbre feature extraction. The development of timbre feature extraction is inseparable from the contribution of machine learning. Machine learning has been applied in timbre feature extraction methods because of its efficient audio data processing advantages. Through comprehensive experimental tests, it could be seen that this machine learning–based timbre feature extraction method was superior to the traditional timbre feature extraction method in all aspects of vocal singing: By extracting the harmonic structure of 8 different types of musical instruments, it could be seen by comparing the harmonic structure diagrams that the harmonic structure could be used to distinguish different musical timbres, which was a very effective feature; by analyzing the user’s current demand for timbre feature extraction, the direction of improvement was determined to focus on timbre extraction optimization in vocal singing; the algorithm was trained and tested through machine learning. After 74 times of learning, the expected error was reached, and the output value was very close to the expected value; the extraction method was improved by machine learning and tested in different environments. The accuracy of the algorithm proposed in this paper has reached more than 80% in all aspects, especially in the vocal singing environment, reaching 82%, which could accurately extract the timbre features; through the group experiment, the improved method accounted for 42% of the satisfaction, which was 33% higher than that before the improvement. It has shown that the timbre extraction method proposed in this paper could meet the needs of users for timbre feature extraction in vocal singing. But there were some problems that can be improved in this experiment. For example, if the sample environment for model training and testing could be replaced with real music data, the accuracy rate should increase. However, due to limited time, this paper did not do this test, hoping to serve as a reference for future research.



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*Source: 5074829-2022-09-17.xml*