# Classification Method of Emotional Characteristics of Vocal Singing under Multicultural Values

Authors: Kai Ji
Journal: Journal of Sensors
 (2022)

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## Abstract

In today’s world economic integration development, the development of cultural pluralism and diversity has become a major trend of social development in this era. In the process of development of the times, China’s vocal singing is constantly adapting to and meeting the growing and changing aesthetic needs of people, while also constantly seeking its own perfection and development and releasing its unique artistic tension. Accordingly, the author explores the inheritance and development of China’s vocal singing from the background of multiculturalism, explores the main characteristics of China’s vocal singing through an in-depth interpretation of the concept of multiculturalism, analyzes and studies the emotional characteristics of vocal singing from the perspective of multiculturalism, and constructs an effective classification method for vocal singing under the value of multiculturalism, in order to bring enlightenment to promote the better development of China’s vocal singing The purpose is to bring enlightenment for the better development of vocal singing in China.


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## Body

## 1. Introduction

The exchange and interaction between various cultures and the fusion of each other’s roles are always taking place. By absorbing and internalizing the essence of other countries or national cultures, we can continuously enrich and develop our own culture and then realize the inheritance and development of our own culture and national culture, so that it can gain a long-lasting cultural vitality [1–5]. Therefore, in the context of the continuous development of the world economic integration, China should also firmly establish the trend of diversified development in the development of traditional culture and art, while preserving the essence of vocal singing, and constantly absorb and learn from the experience and advantages of foreign cultural development, so as to meet the diversified needs of people for culture and art in the current era [6]. At the same time, in the context of multiculturalism, the inheritance and promotion of the spiritual connotation of China’s excellent national culture should fully respect foreign cultures on the basis of national traditions and strive to promote the innovative inheritance and development of China’s traditional arts through artistic multi-fusion.Our vocal singing mainly refers to our national vocal music, which is important in various ancient forms of vocal art expression, and its main presentation form is singing. The development of folk vocal music has a high degree of synchronization with the development of human society and plays a unique charm in the development of human social civilization, which is the most real and direct expression and transmission of people’s emotions [7]. Under the influence of multiculturalism, vocal singing has gradually gone global. In this background, China’s national vocal music shows new characteristics and connotations, mainly in the following aspects. Firstly, vocal singing is spatial and temporal, with both visual and auditory characteristics. Secondly, traditional vocal music originates from practical activities and has scientific theoretical support, which is not only highly performative but also cross-fertilized with other disciplines. Thirdly, vocal singing has a wide mass base and is widely disseminated, which is a distillation of national language and culture, and is the artistic wealth of the whole nation. Fourthly, vocal singing can play a good role in regulating people’s emotion and mentality and has a positive effect of purifying the mind and guiding life [8].China has been promoting the traditional culture of the nation as the most important task of its development, and vocal art can not only promote people to understand the diversity of national culture and have a deeper and more comprehensive understanding of the cultural characteristics of each ethnic region but also help to promote respect and understanding between different regional cultures and understanding between different cultures. In today’s global integration to the deep development, the cross-cultural communication activities between countries in the world are more and more frequent. China’s vocal singing, while paying attention to highlighting national cultural characteristics, has always adhered to the development concept of compatibility and harmony and constantly absorbed the essence of foreign cultures, which has laid a good foundation for its sustainable development [9]. If China’s traditional folk vocal music cannot absorb and learn from the world’s advanced performance techniques, the spring singing skills alone are bound to be rejected by the times. In recent years, China’s national vocal music and Western countries more and more frequent vocal exchanges, China’s vocal singing with our unique national characteristics of a perfect combination, and the formation of a unique national vocal music boutique repertoire, through the wonderful performances around the world, showing the great charm of China’s national vocal art, in the world of performing arts stage shine. Therefore, China’s vocal singing must adhere to the traditional culture on the basis of continuous innovation, in order to open up a new world for the development of China’s vocal singing in the future.


## 2. Related Work

China’s vocal singing has an extremely rich content and expression; in terms of traditional national vocal singing, there are opera, local folk songs, rap and other forms [10, 11]. However, in the current rapid development of social economy and culture, the diversified cultural impact has caused a huge change in people’s ideology and aesthetic interest, which also brings new challenges for the inheritance and development of vocal singing in China [12]. To achieve the development of vocal singing in the new era under the background of diversified culture, we must establish a new aesthetic concept, look at the innovation of vocal singing content and performance forms from a contemporary aesthetic perspective, encourage traditional vocal artists to boldly try the combination of diversified ethnic music forms, bring driving force for the innovative development of vocal singing in China through a new aesthetic, and make our excellent ethnic vocal works [13]. The new aesthetics will bring the driving force for the innovative development of vocal singing in China, so that the excellent folk vocal works in China can be kept fresh and release a new vitality [14].Academic work on the classification of emotion in automated music can be traced back to 1999 [15, 16] in his doctoral dissertation on the time series analysis of emotion in music, where he examined the relationship between emotion and musical characteristics and the changes over time in terms of five categories of characteristics: fundamental period (pitch), tempo, loudness, frequency spectrum centroid, and texture (texture) (the number of types of different instruments) explored the relationship between emotion and musical characteristics and the changes over time. More researchers joined the team, and papers on music emotion classification were published between 2000 and 2006. In 2006, Xie et al. [17] used a two-layer emotion classification model based on Adaboost to classify music. In 2008, Scherer et al. [18] reduced the emotional classification of music to a regression problem, based on the sound quality, melodic, and rhythmic characteristics of the music. In 2016, [19] proposed a color space-based BP neural network remote sensing image classification algorithm. In 2013, [20] proposed a SVM model; firstly, the music is feature extracted, then the features are input into the already trained four-category sentiment model, four decision values are obtained as features of the second layer SVM model, and the final classification result is output. This shows that music emotion classification has received sufficient attention from the academic community.Not only have many scholars joined the field of music emotion classification research, but many universities have also established specialized laboratories. A Music and Audio Computing (MAC) Lab has been established at the National Taiwan University, and researchers in its research lab [21] have published more than 12 core journal papers and 18 conference papers related to music emotion classification and edited the book Music Emotion Recognition with [22] in 2011. In 2011, researchers edited the book Music Emotion Recognition with [23]. Other researchers in the lab have also published several important papers in related research areas.The current changes in the world’s cultural landscape are gradually refreshing people’s mindset, gradually accepting and assimilating multicultural education, and requiring the current vocal singing to reflect on the multicultural background. In the era of cross-fertilization of multiple cultures, the development of vocal singing in isolation from the multicultural background has become impossible. We study the emotional characteristics of vocal singing in a multicultural perspective from the connotation of multiculturalism and multicultural vocal singing and construct an effective classification method for vocal singing under multicultural values.


## 3. Methods

Vocal singing is an expressive art form that requires not only grasping basic vocal singing skills but also expressing various musical emotions with voice, conveying various emotions of characters, and issuing the tones needed to express specific emotions, which shows that emotional expression is very important in vocal singing.Emotion classification of music mainly includes the following parts: (i) determination of music emotion category, (ii) feature extraction of music fragments, (iii) training of music emotion classification model, and (iv) classification of test set using the generated model as is in Figure1.Figure 1
Flowchart of music for emotion classification.Constant-Q chromatograms are obtained by transforming each discourse, and the center frequency is defined in Equations (1) and (2).
(1)fk=f1∙2k/b,(2)akn=1CnNkexpi2πnfkfs+Φk,wherewt is a window function similar to the Hanning window and Φk denotes the phase shift, where the scale factor C is defined as (3.3) is shown.
(3)C=∑l=−Nk/2Nk/2wl+Nk/2Nk.To implement the above process, this section makes use of the audio processing toolkit Librosa to complete the extraction of audio files corresponding to Constant-Q chromatograms. We set the number of samples between consecutive chromatic frames to 512 and the size of the chromatogram to120×120×3. To facilitate model training, all Constant-Q chromatograms are normalized in this section to obtain the Constant-Q chromatogram data for subsequent feature extraction.In order to obtain rich emotion-related information from the Constant-Q chromatogram, this chapter proposes a Contextual Residual LSTM Attention Model, which is mainly divided into two phases: constant-Q chromatogram feature extraction and contextual representation learning. In the feature extraction stage, this section uses the ResNet network to learn the corresponding feature representations from the Constant-Q chromatogram; in the representation learning stage, this section constructs the Contextual Residual LSTM Attention Model (CRLA) based on the feature representations learned by the ResNet network, which uses the LSTM network to learn contextual information between discourses, and introduces self-attention to capture emotionally salient information and input to the network for assisting the learning of sentiment representations.The Constant-Q chromatogram is a representation of audio data, which contains rich emotional information, and in order to capture the emotional features in it, this section introduces the ResNet network to learn the spectrogram features from it. The ResNet network is a widely used picture classification model, which directly passes the current output to the next layer of the network by adding a constant mapping to the network. Thus, the gradient disappearance problem is well solved. The network is a kind of skip-connected network, which avoids gradient explosion and gradient disappearance and trains deeper networks by skipping the previous activation value to the intermediate network layer and directly passing it to the later network.As shown in Figure2, firstly, Constant-O chromatogram will pass through a 7×7 convolutional layer, followed by a 3×3 maximum pooling layer, and then, it will pass through 16 residual blocks, all of which have a convolutional kernel size of 3×3, and the number of convolutional kernels increases from 64 to 512, and finally, a 512-dimensional vector is obtained through a global average pooling layer. Vector is used as the audio features extracted from the Constant-Q chromatogram for subsequent classification tasks.Figure 2
Self-attention model structure diagram.In this section, the residual connection mechanism is used to connect the context information learned by the two-layer Bi-LSTM network for it effectively alleviates the bottleneck problem that the deep network is difficult to train because residual concatenation changes the representation of successive multiplication of gradients in network back propagation. After splicing, the model fully integrates the contextual information by using a fully connected layer and employs self-attention to capture the emotionally significant information in the audio data. Figure2 shows the model structure of self-attention.After obtaining the output of self-attention, this section splices it so as to fuse the sentiment salient information with the contextual information, and finally, the fused information is input to the output layer to perform sentiment. Finally, the fused information is fed to the output layer for sentiment classification.Figure3 shows the comparison between the original music signal spectrum and the preemphasis processed music spectrum; it can be seen that the high frequency part of the spectrum is boosted after the preemphasis.Figure 3
Speech signal spectrum after preemphasis processing.This makes it possible to divide the music signal into some short segments for processing. Usually, a moving finite length window for weighting is used to realize the music signal framing. It can be divided into approximately 33-100 frames per second. The music signal can be divided into consecutive or overlapping segments. The advantage of using overlapping segmentation is that the transition between the two frames can be smooth and continuous.Figure4 shows that the rectangular window has a small main flap width, but its side flap height is high; the Hamming window has a wide main flap and a low side flap height. The rectangular window has a high paraflap, which can produce serious leakage (Gibbs), and is therefore used in only a few cases.Figure 4
Waveforms and frequency response of rectangular window and Hamming window.In this paper, three models need to be generated for the music classification system: a fast and slow rhythm classification model (abbreviated as model1, with 1 indicating that it is the first level classifier), a rousing and pleasurable category classifier (abbreviated as epmodel2, with “e” being the first letter of excited and “p” being the first letter of p. “p” is the first letter of pleasured, and 2 indicates that it is the second layer classifier), and the sad and calm category classifier (abbreviated as csmodel2, and similarly, “c” is the first letter of calm, and “s” is the first letter of sad, and 2 indicates a second layer classifier). The model generation flow chart is shown in Figure5. And the flow for sentiment classification is shown in Figure 6. From the model generation flowchart, it can be seen that the first step of the whole music classification system is to manually calibrate the whole music library, i.e., the song target with obvious emotion recognition in the music library is one of the four categories: exciting, pleasant, calm, and sad.Figure 5
Model generation flow chart.Figure 6
Flow chart of music clip emotion classification.


## 4. Experiments

### 4.1. Energy Feature Value

The connection between energy and phonetic emotion was analyzed by Xuesheng Jin, and the relationship between energy and musical emotion will be analyzed more closely here. Although there is a vocal part in the music clip, the voice emotion expression of the usual speaker is still different from the emotion expression in the music clip. We analyze the root-mean-square energy spectra for four emotional musical pieces: excitement, pleasure, sadness, and calmness. The root-mean-square energy is calculated using Equation (4).
(4)xrms=1n∑i=1nxi2=x12+x22+⋯+xn2n.From the results, we can also find that the average energy value of sad songs is smaller in 5 s, but the energy value of calm songs is larger, which may be related to factors such as the volume of recording equipment mentioned in the previous section. However, we can find another pattern in the figure, the energy spectrum of the exciting songs and the pleasant songs is relatively smooth, and the peak, valley, and range fluctuate more, and the peak, valley, and range of energy change more in each interval time. This phenomenon is due to the fact that the rhythms of exciting and pleasant music clips are faster and maintain the same more active emotion for a longer time interval, with less fluctuation, while the rhythms of calm and sad music clips are slower, with more emotional fluctuation, and there may be longer periods of low volume or only accompaniment in the whole music clip. For example, the singer of a sad song may suddenly increase the volume and raise the frequency to express the emotion, comparing the sad. Based on the characteristics of this pattern, we can use the mean and variance statistical features of energy for the first layer of the classification system designed in this paper and classify the music clips into fast-paced categories and slow-paced categories.


### 4.2. Base Tone Eigenvalues

The analysis of the link between the fundamental frequency of speech signals and emotional expression shows that the fundamental frequency characteristics of normal speech signals have an important link with emotional expression, and there are large differences in the range of fundamental frequencies between speakers of different genders. This system belongs to audio signal, but still there is a difference from the usual speech signal, so this paper does further experimental simulation for the connection between the fundamental frequency characteristics of music signal and the emotion expression. In this experiment, the music of four emotion categories in the test set and training set is further categorized into two subfolders, female and male, which store the songs sung by female singers and male singers under the current emotion category. Then, we extracted the fundamental frequency eigenvalues of the songs sung by female and male singers under each emotion, and the obtained statistics are shown in Table1 below. For the convenience of observation and analysis, the three statistical characteristic data in Table 1 were plotted as distribution curves in Figures 7(a)–7(c) according to different emotional states.Table 1
Fundamental frequency characteristics of music fragments in different emotional states.
FO_maxFO_meanFO_minMaleFemaleMaleFemaleMaleFemaleCheerful478.98483.43239.93286.1978.6377.94Calm474.29475.41226.85253.8575.7776.88Sad473.59479.52232.06275.6975.1676.67Impassioned46.42483.96243.19294.6576.6878.19Figure 7
(a) Distribution trend of F0_max. (b) Trend of distribution of F0_mean. (c) Trend of the distribution of F0_min.
(a)(b)(c)From Figures7(a)–7(c), it can be seen that the relative trends of the distribution of the three statistical characteristics of the maximum, mean, and minimum values of the fundamental frequencies under different emotional states are relatively similar for both male and female singers. The values of the three fundamental frequency statistical characteristics are higher for the pleasant and exhilarating songs than for the calm and sad songs. Because the pitch of the singer increases in the pleasant or exciting songs and decreases in the other two states, there is still a pitch increase in the sad songs, and the mean value of the fundamental frequency is greater than that of the calm songs. It is obvious from the graph that the fundamental frequencies of songs sung by women are generally greater than those of men. If the statistical characteristics of the fundamental frequency of the music fragments are extracted as the characteristic values of the music fragments, and the gender of the music singers is not classified, there may be some errors. For this argument, experimental simulations will be conducted in the following subsections.


### 4.3. Timbral Feature Value Extraction

The short-time spectral features proposed in the timbre feature extraction and analysis of music signals have been used in the literature (Adaboost-based music emotion classification), pointing out that such features portray the short-time spectral shape of music from various angles, accurately grasp the characteristics of the short-time spectrum, and achieve good results in music emotion classification. This paper will also use this feature to classify the emotions of music.In this subsection, experiments are designed to simulate this idea to get the final conclusion. The first case is to conduct experiments without singer gender classification, and the second case is to conduct experiments with singer gender classification and get the final experimental results.


#### 4.3.1. The Case of Not Distinguishing the Singer’s Gender

In this case, the format of storing all test set songs in the system is unified as follows: for the songs of the training set, create the folder Training, create four subfolders under this folder, respectively, pleased, calm, sad, and excited, and store the corresponding songs in the corresponding directory; for the songs of the test set, create the folder Testing, and similarly, create four subfolders under the Testing folder, and store the manually calibrated songs of the test set in the corresponding folders. For the test set songs, create the folder Testing, similarly, create four subfolders pleased, calm, sad, and excited under the Testing folder, and store the manually calibrated songs of different categories of the test set in the corresponding folders. If you only test a single music clip, you only need to add this music clip to the current directory.Table2 shows the classification accuracies of the three models. From the table, the accuracy of the Adaboost classification algorithm is significantly higher than that of the SVM, which should confirm that finding multiple weak classifiers combined to form a strong classifier has a significant improvement in classification accuracy than finding a strong classification alone. The accuracy of csmodel2 is low compared to the other two models because the songs included in the calm category and the sad category may have different classification results even for different people. The difference in votes between the two categories would not be very large if the results were determined based on multiple volunteer voting statistics. Distinguishing between the two categories, which have considerable overlap, is of course a difficult task. The accuracy of model1 is the highest regardless of whether the SVM algorithm or Adaboost algorithm is used. The reason is that the difference between the songs in the fast tempo category and the slow tempo category is relatively large, and people can easily distinguish between the two categories. The feature values extracted in the previous subsection can also accurately distinguish between the two categories, so the accuracy of the Adaboost algorithm can reach 0.913. This also lays the foundation for the success of the two-layer emotion classification system structure used in this paper. In order to be able to achieve a better classification of the four musical emotions, it is necessary to achieve a higher accuracy in the first layer of the system structure so that there can be a higher accuracy in the second layer of the classification.Table 2
Classification accuracy of the three models generated by Adaboost and SVM.
Classification methodModelAccuracySVMAdaboostmodel10.8060.913csmodel20.6520.758epmodel20.6930.782Table3 shows the classification accuracies of the four sentiment categories for the whole test set. The data in the table show that the number of classification errors for the pleasant and exciting songs is higher than that for the calm and sad songs, which is not consistent with the classification accuracy of each model in Table 2. The number of incorrect songs in the first level of classification is smaller, and the accuracy rate is higher, but the number of incorrect songs in the fast-paced category is larger and the accuracy rate is not high. In contrast, when the Adaboost classification algorithm was used, it did not have a large negative impact when classifying music in the second level because the accuracy of the classification results in the first level had reached 0.913 and the number of incorrectly classified songs was very low.Table 3
Classification accuracy of the four emotional categories for the test set.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful62/1240.50022/1240.823Calm32/1120.71428/1120.750Sad58/1180.50838/1180.755Impassioned62/1280.46535/1280.726Table4 shows the specific classification results of the Adaboost algorithm for the four emotion categories.Table 4
Adaboost algorithm specific classification results.
CategoryCheerfulCalmSadImpassionedCheerful1021408Calm985180Sad1218214Impassioned293393For the calm category and the sad category, the songs of these two categories may also get different classification results for different people, and it is certainly a difficult task for the computer to distinguish between these two categories with more overlapping areas.The following comparative experiments are also conducted in this paper. For the same music database with the same feature values, the results of the experiments using the system structure approach in the literature [20] are as follows (see Table 5).Table 5
Experimental results using the system structure in the literature [20].
CategoryCheerfulCalmSadImpassionedCheerful80191213Calm571324Sad3247110Impassioned3231776


#### 4.3.2. Distinguishing the Gender of Singers

In this case, all the songs of the test set are stored in the following format: for the songs of the training set, create the folder Training, create two subfolders male and female under the folder, create four subfolders under the folders male and female, respectively, pleased, calm, sad, and excited, and store the corresponding songs in the corresponding directories; for the songs of the test set, create the folder Testing, also create two subfolders male and female under the folder Testing, and create four subfolders under the folders male and female, respectively. For the test set songs, create the folder Testing, also create two subfolders male and female under the Testing folder, create four subfolders under the subfolders male and female, respectively, pleased, calm, sad, and excited, and store the manually calibrated. The songs of different categories of the test set are stored in the corresponding folders. If you are testing only a single music clip, you only need to add this music clip to the current directory [24, 25].Tables6 and 7 show the classification accuracy of the three models generated by Adaboost classification algorithm and SVM classification algorithm for different gender test sets, respectively. It is seen that the accuracy of model1, csmodel2, and epmodel2 has improved, although the improvement is not very large, which proves the correctness of the point mentioned that there are differences in the feature values of the music clips sung by different gender singers. The accuracy of the sentiment classification by the gender of the singer is improved.Table 6
The classification accuracies of the three models for the music clips sung by female singers in the whole test set.
Classification methodModelAccuracySVMAdaboostmodel10.8360.978csmodel20.6650.776epmodel20.7130.792Table 7
The classification accuracy of the three models for music clips with male singers in the entire test set.
Classification methodModelAccuracySVMAdaboostmodel10.8300.963csmodel20.6580.762epmodel20.7070.798Tables8 and 9 show the classification accuracies for the four sentiment categories for the entire test set with female and male singers, respectively. The accuracy of both the test set with male singers and the test set with female singers has improved, and the same Adaboost classification algorithm has higher accuracy than the SVM classification algorithm, which is consistent with the classification results of both algorithms for the whole test set without gender differentiation.Table 8
Classification accuracies for the four sentiment categories with female singers in the entire test set.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful37/800.53810/800.875Calm14/560.7514/560.75Sad33/680.51518/680.765Impassioned26/570.54515/570.736Table 9
Classification accuracy for the four affective categories in the entire test set where the singer was male.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful22/440.5007/440.841Calm14/540.74114/540.741Sad26/530.51514/530.736Impassioned31/670.53818/670.732The results of the simulation experiments in the above two cases (i.e., distinguishing the gender of the singer and not distinguishing the gender of the singer) show that the Adaboost classification algorithm performs better than the SVM classification algorithm. This is because the SVM method directly finds an optimal hyperplane to distinguish the two categories of music and uses it as the final classifier. However, classifying music for sentiment is inherently very ambiguous, and artificial sentiment calibration for music is highly subjective, and the same music fragment can likely be classified as different sentiments. It is difficult to find a strong classifier with high classification accuracy for music emotion classification, which greatly improves the performance.


## 4.1. Energy Feature Value

The connection between energy and phonetic emotion was analyzed by Xuesheng Jin, and the relationship between energy and musical emotion will be analyzed more closely here. Although there is a vocal part in the music clip, the voice emotion expression of the usual speaker is still different from the emotion expression in the music clip. We analyze the root-mean-square energy spectra for four emotional musical pieces: excitement, pleasure, sadness, and calmness. The root-mean-square energy is calculated using Equation (4).
(4)xrms=1n∑i=1nxi2=x12+x22+⋯+xn2n.From the results, we can also find that the average energy value of sad songs is smaller in 5 s, but the energy value of calm songs is larger, which may be related to factors such as the volume of recording equipment mentioned in the previous section. However, we can find another pattern in the figure, the energy spectrum of the exciting songs and the pleasant songs is relatively smooth, and the peak, valley, and range fluctuate more, and the peak, valley, and range of energy change more in each interval time. This phenomenon is due to the fact that the rhythms of exciting and pleasant music clips are faster and maintain the same more active emotion for a longer time interval, with less fluctuation, while the rhythms of calm and sad music clips are slower, with more emotional fluctuation, and there may be longer periods of low volume or only accompaniment in the whole music clip. For example, the singer of a sad song may suddenly increase the volume and raise the frequency to express the emotion, comparing the sad. Based on the characteristics of this pattern, we can use the mean and variance statistical features of energy for the first layer of the classification system designed in this paper and classify the music clips into fast-paced categories and slow-paced categories.


## 4.2. Base Tone Eigenvalues

The analysis of the link between the fundamental frequency of speech signals and emotional expression shows that the fundamental frequency characteristics of normal speech signals have an important link with emotional expression, and there are large differences in the range of fundamental frequencies between speakers of different genders. This system belongs to audio signal, but still there is a difference from the usual speech signal, so this paper does further experimental simulation for the connection between the fundamental frequency characteristics of music signal and the emotion expression. In this experiment, the music of four emotion categories in the test set and training set is further categorized into two subfolders, female and male, which store the songs sung by female singers and male singers under the current emotion category. Then, we extracted the fundamental frequency eigenvalues of the songs sung by female and male singers under each emotion, and the obtained statistics are shown in Table1 below. For the convenience of observation and analysis, the three statistical characteristic data in Table 1 were plotted as distribution curves in Figures 7(a)–7(c) according to different emotional states.Table 1
Fundamental frequency characteristics of music fragments in different emotional states.
FO_maxFO_meanFO_minMaleFemaleMaleFemaleMaleFemaleCheerful478.98483.43239.93286.1978.6377.94Calm474.29475.41226.85253.8575.7776.88Sad473.59479.52232.06275.6975.1676.67Impassioned46.42483.96243.19294.6576.6878.19Figure 7
(a) Distribution trend of F0_max. (b) Trend of distribution of F0_mean. (c) Trend of the distribution of F0_min.
(a)(b)(c)From Figures7(a)–7(c), it can be seen that the relative trends of the distribution of the three statistical characteristics of the maximum, mean, and minimum values of the fundamental frequencies under different emotional states are relatively similar for both male and female singers. The values of the three fundamental frequency statistical characteristics are higher for the pleasant and exhilarating songs than for the calm and sad songs. Because the pitch of the singer increases in the pleasant or exciting songs and decreases in the other two states, there is still a pitch increase in the sad songs, and the mean value of the fundamental frequency is greater than that of the calm songs. It is obvious from the graph that the fundamental frequencies of songs sung by women are generally greater than those of men. If the statistical characteristics of the fundamental frequency of the music fragments are extracted as the characteristic values of the music fragments, and the gender of the music singers is not classified, there may be some errors. For this argument, experimental simulations will be conducted in the following subsections.


## 4.3. Timbral Feature Value Extraction

The short-time spectral features proposed in the timbre feature extraction and analysis of music signals have been used in the literature (Adaboost-based music emotion classification), pointing out that such features portray the short-time spectral shape of music from various angles, accurately grasp the characteristics of the short-time spectrum, and achieve good results in music emotion classification. This paper will also use this feature to classify the emotions of music.In this subsection, experiments are designed to simulate this idea to get the final conclusion. The first case is to conduct experiments without singer gender classification, and the second case is to conduct experiments with singer gender classification and get the final experimental results.


### 4.3.1. The Case of Not Distinguishing the Singer’s Gender

In this case, the format of storing all test set songs in the system is unified as follows: for the songs of the training set, create the folder Training, create four subfolders under this folder, respectively, pleased, calm, sad, and excited, and store the corresponding songs in the corresponding directory; for the songs of the test set, create the folder Testing, and similarly, create four subfolders under the Testing folder, and store the manually calibrated songs of the test set in the corresponding folders. For the test set songs, create the folder Testing, similarly, create four subfolders pleased, calm, sad, and excited under the Testing folder, and store the manually calibrated songs of different categories of the test set in the corresponding folders. If you only test a single music clip, you only need to add this music clip to the current directory.Table2 shows the classification accuracies of the three models. From the table, the accuracy of the Adaboost classification algorithm is significantly higher than that of the SVM, which should confirm that finding multiple weak classifiers combined to form a strong classifier has a significant improvement in classification accuracy than finding a strong classification alone. The accuracy of csmodel2 is low compared to the other two models because the songs included in the calm category and the sad category may have different classification results even for different people. The difference in votes between the two categories would not be very large if the results were determined based on multiple volunteer voting statistics. Distinguishing between the two categories, which have considerable overlap, is of course a difficult task. The accuracy of model1 is the highest regardless of whether the SVM algorithm or Adaboost algorithm is used. The reason is that the difference between the songs in the fast tempo category and the slow tempo category is relatively large, and people can easily distinguish between the two categories. The feature values extracted in the previous subsection can also accurately distinguish between the two categories, so the accuracy of the Adaboost algorithm can reach 0.913. This also lays the foundation for the success of the two-layer emotion classification system structure used in this paper. In order to be able to achieve a better classification of the four musical emotions, it is necessary to achieve a higher accuracy in the first layer of the system structure so that there can be a higher accuracy in the second layer of the classification.Table 2
Classification accuracy of the three models generated by Adaboost and SVM.
Classification methodModelAccuracySVMAdaboostmodel10.8060.913csmodel20.6520.758epmodel20.6930.782Table3 shows the classification accuracies of the four sentiment categories for the whole test set. The data in the table show that the number of classification errors for the pleasant and exciting songs is higher than that for the calm and sad songs, which is not consistent with the classification accuracy of each model in Table 2. The number of incorrect songs in the first level of classification is smaller, and the accuracy rate is higher, but the number of incorrect songs in the fast-paced category is larger and the accuracy rate is not high. In contrast, when the Adaboost classification algorithm was used, it did not have a large negative impact when classifying music in the second level because the accuracy of the classification results in the first level had reached 0.913 and the number of incorrectly classified songs was very low.Table 3
Classification accuracy of the four emotional categories for the test set.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful62/1240.50022/1240.823Calm32/1120.71428/1120.750Sad58/1180.50838/1180.755Impassioned62/1280.46535/1280.726Table4 shows the specific classification results of the Adaboost algorithm for the four emotion categories.Table 4
Adaboost algorithm specific classification results.
CategoryCheerfulCalmSadImpassionedCheerful1021408Calm985180Sad1218214Impassioned293393For the calm category and the sad category, the songs of these two categories may also get different classification results for different people, and it is certainly a difficult task for the computer to distinguish between these two categories with more overlapping areas.The following comparative experiments are also conducted in this paper. For the same music database with the same feature values, the results of the experiments using the system structure approach in the literature [20] are as follows (see Table 5).Table 5
Experimental results using the system structure in the literature [20].
CategoryCheerfulCalmSadImpassionedCheerful80191213Calm571324Sad3247110Impassioned3231776


### 4.3.2. Distinguishing the Gender of Singers

In this case, all the songs of the test set are stored in the following format: for the songs of the training set, create the folder Training, create two subfolders male and female under the folder, create four subfolders under the folders male and female, respectively, pleased, calm, sad, and excited, and store the corresponding songs in the corresponding directories; for the songs of the test set, create the folder Testing, also create two subfolders male and female under the folder Testing, and create four subfolders under the folders male and female, respectively. For the test set songs, create the folder Testing, also create two subfolders male and female under the Testing folder, create four subfolders under the subfolders male and female, respectively, pleased, calm, sad, and excited, and store the manually calibrated. The songs of different categories of the test set are stored in the corresponding folders. If you are testing only a single music clip, you only need to add this music clip to the current directory [24, 25].Tables6 and 7 show the classification accuracy of the three models generated by Adaboost classification algorithm and SVM classification algorithm for different gender test sets, respectively. It is seen that the accuracy of model1, csmodel2, and epmodel2 has improved, although the improvement is not very large, which proves the correctness of the point mentioned that there are differences in the feature values of the music clips sung by different gender singers. The accuracy of the sentiment classification by the gender of the singer is improved.Table 6
The classification accuracies of the three models for the music clips sung by female singers in the whole test set.
Classification methodModelAccuracySVMAdaboostmodel10.8360.978csmodel20.6650.776epmodel20.7130.792Table 7
The classification accuracy of the three models for music clips with male singers in the entire test set.
Classification methodModelAccuracySVMAdaboostmodel10.8300.963csmodel20.6580.762epmodel20.7070.798Tables8 and 9 show the classification accuracies for the four sentiment categories for the entire test set with female and male singers, respectively. The accuracy of both the test set with male singers and the test set with female singers has improved, and the same Adaboost classification algorithm has higher accuracy than the SVM classification algorithm, which is consistent with the classification results of both algorithms for the whole test set without gender differentiation.Table 8
Classification accuracies for the four sentiment categories with female singers in the entire test set.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful37/800.53810/800.875Calm14/560.7514/560.75Sad33/680.51518/680.765Impassioned26/570.54515/570.736Table 9
Classification accuracy for the four affective categories in the entire test set where the singer was male.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful22/440.5007/440.841Calm14/540.74114/540.741Sad26/530.51514/530.736Impassioned31/670.53818/670.732The results of the simulation experiments in the above two cases (i.e., distinguishing the gender of the singer and not distinguishing the gender of the singer) show that the Adaboost classification algorithm performs better than the SVM classification algorithm. This is because the SVM method directly finds an optimal hyperplane to distinguish the two categories of music and uses it as the final classifier. However, classifying music for sentiment is inherently very ambiguous, and artificial sentiment calibration for music is highly subjective, and the same music fragment can likely be classified as different sentiments. It is difficult to find a strong classifier with high classification accuracy for music emotion classification, which greatly improves the performance.


## 4.3.1. The Case of Not Distinguishing the Singer’s Gender

In this case, the format of storing all test set songs in the system is unified as follows: for the songs of the training set, create the folder Training, create four subfolders under this folder, respectively, pleased, calm, sad, and excited, and store the corresponding songs in the corresponding directory; for the songs of the test set, create the folder Testing, and similarly, create four subfolders under the Testing folder, and store the manually calibrated songs of the test set in the corresponding folders. For the test set songs, create the folder Testing, similarly, create four subfolders pleased, calm, sad, and excited under the Testing folder, and store the manually calibrated songs of different categories of the test set in the corresponding folders. If you only test a single music clip, you only need to add this music clip to the current directory.Table2 shows the classification accuracies of the three models. From the table, the accuracy of the Adaboost classification algorithm is significantly higher than that of the SVM, which should confirm that finding multiple weak classifiers combined to form a strong classifier has a significant improvement in classification accuracy than finding a strong classification alone. The accuracy of csmodel2 is low compared to the other two models because the songs included in the calm category and the sad category may have different classification results even for different people. The difference in votes between the two categories would not be very large if the results were determined based on multiple volunteer voting statistics. Distinguishing between the two categories, which have considerable overlap, is of course a difficult task. The accuracy of model1 is the highest regardless of whether the SVM algorithm or Adaboost algorithm is used. The reason is that the difference between the songs in the fast tempo category and the slow tempo category is relatively large, and people can easily distinguish between the two categories. The feature values extracted in the previous subsection can also accurately distinguish between the two categories, so the accuracy of the Adaboost algorithm can reach 0.913. This also lays the foundation for the success of the two-layer emotion classification system structure used in this paper. In order to be able to achieve a better classification of the four musical emotions, it is necessary to achieve a higher accuracy in the first layer of the system structure so that there can be a higher accuracy in the second layer of the classification.Table 2
Classification accuracy of the three models generated by Adaboost and SVM.
Classification methodModelAccuracySVMAdaboostmodel10.8060.913csmodel20.6520.758epmodel20.6930.782Table3 shows the classification accuracies of the four sentiment categories for the whole test set. The data in the table show that the number of classification errors for the pleasant and exciting songs is higher than that for the calm and sad songs, which is not consistent with the classification accuracy of each model in Table 2. The number of incorrect songs in the first level of classification is smaller, and the accuracy rate is higher, but the number of incorrect songs in the fast-paced category is larger and the accuracy rate is not high. In contrast, when the Adaboost classification algorithm was used, it did not have a large negative impact when classifying music in the second level because the accuracy of the classification results in the first level had reached 0.913 and the number of incorrectly classified songs was very low.Table 3
Classification accuracy of the four emotional categories for the test set.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful62/1240.50022/1240.823Calm32/1120.71428/1120.750Sad58/1180.50838/1180.755Impassioned62/1280.46535/1280.726Table4 shows the specific classification results of the Adaboost algorithm for the four emotion categories.Table 4
Adaboost algorithm specific classification results.
CategoryCheerfulCalmSadImpassionedCheerful1021408Calm985180Sad1218214Impassioned293393For the calm category and the sad category, the songs of these two categories may also get different classification results for different people, and it is certainly a difficult task for the computer to distinguish between these two categories with more overlapping areas.The following comparative experiments are also conducted in this paper. For the same music database with the same feature values, the results of the experiments using the system structure approach in the literature [20] are as follows (see Table 5).Table 5
Experimental results using the system structure in the literature [20].
CategoryCheerfulCalmSadImpassionedCheerful80191213Calm571324Sad3247110Impassioned3231776


## 4.3.2. Distinguishing the Gender of Singers

In this case, all the songs of the test set are stored in the following format: for the songs of the training set, create the folder Training, create two subfolders male and female under the folder, create four subfolders under the folders male and female, respectively, pleased, calm, sad, and excited, and store the corresponding songs in the corresponding directories; for the songs of the test set, create the folder Testing, also create two subfolders male and female under the folder Testing, and create four subfolders under the folders male and female, respectively. For the test set songs, create the folder Testing, also create two subfolders male and female under the Testing folder, create four subfolders under the subfolders male and female, respectively, pleased, calm, sad, and excited, and store the manually calibrated. The songs of different categories of the test set are stored in the corresponding folders. If you are testing only a single music clip, you only need to add this music clip to the current directory [24, 25].Tables6 and 7 show the classification accuracy of the three models generated by Adaboost classification algorithm and SVM classification algorithm for different gender test sets, respectively. It is seen that the accuracy of model1, csmodel2, and epmodel2 has improved, although the improvement is not very large, which proves the correctness of the point mentioned that there are differences in the feature values of the music clips sung by different gender singers. The accuracy of the sentiment classification by the gender of the singer is improved.Table 6
The classification accuracies of the three models for the music clips sung by female singers in the whole test set.
Classification methodModelAccuracySVMAdaboostmodel10.8360.978csmodel20.6650.776epmodel20.7130.792Table 7
The classification accuracy of the three models for music clips with male singers in the entire test set.
Classification methodModelAccuracySVMAdaboostmodel10.8300.963csmodel20.6580.762epmodel20.7070.798Tables8 and 9 show the classification accuracies for the four sentiment categories for the entire test set with female and male singers, respectively. The accuracy of both the test set with male singers and the test set with female singers has improved, and the same Adaboost classification algorithm has higher accuracy than the SVM classification algorithm, which is consistent with the classification results of both algorithms for the whole test set without gender differentiation.Table 8
Classification accuracies for the four sentiment categories with female singers in the entire test set.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful37/800.53810/800.875Calm14/560.7514/560.75Sad33/680.51518/680.765Impassioned26/570.54515/570.736Table 9
Classification accuracy for the four affective categories in the entire test set where the singer was male.
Classification methodCategorySVMAdaboostNumber of wrong songsAccuracyNumber of wrong songsAccuracyCheerful22/440.5007/440.841Calm14/540.74114/540.741Sad26/530.51514/530.736Impassioned31/670.53818/670.732The results of the simulation experiments in the above two cases (i.e., distinguishing the gender of the singer and not distinguishing the gender of the singer) show that the Adaboost classification algorithm performs better than the SVM classification algorithm. This is because the SVM method directly finds an optimal hyperplane to distinguish the two categories of music and uses it as the final classifier. However, classifying music for sentiment is inherently very ambiguous, and artificial sentiment calibration for music is highly subjective, and the same music fragment can likely be classified as different sentiments. It is difficult to find a strong classifier with high classification accuracy for music emotion classification, which greatly improves the performance.


## 5. Conclusion

The performance of emotion in vocal singing helps deepen the audience’s appreciation experience and realize the ultimate goal of vocal singing. Therefore, it is necessary to deeply analyze the vocal works and understand the thoughts and emotions of the lyrics before singing. It is necessary to keep the breath adequate and coherent, to pursue the rendering of atmosphere and emotion, to decorate and embellish one’s voice, and to correctly express emotion through the accurate grasp of musical elements such as rhythm, speed, strength, and timbre, so as to enhance the artistic impact of vocal singing. In addition to innovating and developing the content and form of folk music performances, meeting the rich aesthetic needs of modern people, and constructing scientific teaching methods and strategies, the inheritance and development of our vocal music must be based on our fundamental national conditions in the context of diversity in the new era.



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*Source: 8168970-2022-07-22.xml*