# Learning Chinese Classical Music with the Aid of Soundscape by Using Intelligent Network

Authors: LiLan Zhang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

A soundscape is a sound environment of the awareness of auditory perception and social or cultural understanding. To improve the subjective initiative of the Chinese classical music students, this study explores new learning modes and methods using soundscape and investigates its learning effect using intelligent music software. To examine the emotional experience of players in playing before and after learning, 50 students from music majors and 50 from non-music majors were selected. Results show that in the positive and negative emotional classical music experiment, most music majors and non-music majors have weak emotional experience at the beginning, and only a few have a strong emotional experience, which could reach 6 points. In the second scoring, most majors have a score of about 7 points, indicating strong emotional experience and a few have a score of about 4 points and 9 points, representing that there were relatively few majors with weak emotional experience and strong emotional experience. The overall emotional experience score is low in the comparison between non-music majors and music majors, and the second score in the entire experiment is significantly higher than the first score, signifying that the learning effect of players is obvious, and intelligent music software and soundscape play a role in the exploration of Chinese classical music.


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## Body

## 1. Introduction

An intelligent network (IN) is a network architecture that quickly, economically, conveniently, and effectively generates and provides intelligent services on communication networks [1]. INs are used in a variety of industries, including medicine, finance, robotics, and law. At the moment, intelligent music software and intelligent electronic instruments are the most common applications of IN in music education. Artificial intelligence and electronic instruments are capable of not only arranging but also storing various soundscapes in music [2]. Intelligent music software can better process the relevant music tasks in the process of music learning. Such music software can edit and modify music and can also use its recording function to intelligently process various music elements. Furthermore, students can play music independently using music software, which allows them to gain a better comprehension of the music knowledge they have gained. Students’ interest in music can be stimulated by using intelligent music software, which allows them to perceive music through the music system [3].In recent years, the soundscape has received much attention in music learning and appreciation. The concept of the soundscape was first proposed by the composer R. Murray Schafer, which advocates preserving the original appearance and emphasizing the relationship between sound and space [4]. The study of soundscape focuses on how people perceive their acoustic surroundings. This disciplinary approach contrasts with the field of noise control, which focuses on the human response to loudness and discomfort caused by exposure to external noise. Soundscape’s larger understanding of auditory experience suggests the possibility of merging findings from affect, emotion, and appraisal research, especially as both noise and soundscape studies currently use similar terminology and concepts [5].Some scholars have explored the application of soundscape in musical expression, and in the field of music, soundscape researchers investigated listeners’ musical cognition and expressive techniques of soundscape and explored the intersection between soundscape performance types and other performance types [6]. Ju et al. [7] presented that the soundscape retains more information, so it is easier for audience to enjoy the happiness and hope of the life when listening the music. Fan et al. [8] compared the Western Classical Music Excerpts (WCMED) and the Corpus of Chinese Classical Music Excerpts (CCMED) and annotated through emotional matching and arousal, and the perceived emotions of WCMED and CCMED were predicted using sound event detection (SED) and soundscape emotion recognition (SER) models as well as migration learning. The design of suitable scenes helps to better feel the music and recognize music, and the use of soundscape to learn music can improve the learning efficiency of music. Wang [9] explored the role of soundscape in environmental art design and proposed that the soundscape improves the overall quality of the human living environment. Quiroz et al. [10] used a neural network to optimize the sampling of soundscapes of three Colombian ecosystems. The neural network was trained to identify meaningful temporal windows for audio recording from previously gathered data. This method was effective in simplifying the acoustic complexity analysis.Another strategy for mapping audio elements of soundscape recordings onto the 2D emotional space was examined by Lundén et al. [11]. The authors took 93 excerpts from 77 soundscape recordings and asked 33 people to score them on a two-dimensional emotional scale. To cluster acoustic features, a Gaussian mixture model is used.Music can affect people’s emotions through tones, and music is also an expression of emotions [12]. There are two kinds of emotions related to music, one is the recognition and judgment of musically expressed emotions, and the other is emotions induced by music [13]. Musically induced emotions have aesthetic characteristics and are divided into curiosity, detachment, tenderness, nostalgia, calm strength, joy, and uplift [14]. The measurement methods of music-induced emotions are self-report, physiological psychometry, and functional neuroimaging.This study, which focuses on the learning of Chinese classical music, investigates the learning effects of students who are assisted by intelligent music software and soundscape, resulting in new models and ideas for learning Chinese classical music. Because individual emotional experience is the most direct manifestation of music expression, it assesses the learning effect by examining the students’ emotional experiences. The emotional experience score of 50 students majoring in music and 50 students majoring in non-music in two performances of Chinese classical music was used to assess the learning effect. The proposed intelligent music software and soundscape play a key role in the exploration of Chinese classical music.The rest of the paper is organized as follows. Section2 is about materials and methods and provides a detailed description of the proposed method. Section 3 is about the results and discussion, and the conclusion is given in Section 4.


## 2. Materials and Methods

### 2.1. Intelligent Network

IN is a kind of superposition network introduced in the traditional mobile exchange network to provide value-added services more flexibly, conveniently, economically, and effectively. The whole mobile network forms a network structure composed of the original exchange layer and the superposed intelligent layer. The exchange layer is responsible for completing the basic service call connection function, while some more complex value-added services have the intelligent layer for control realization [15]. The separation of the exchange and control functions is the most important feature of IN. Intelligent speech recognition technology has made significant progress in recent years, and it is expected that speech recognition technology will enter various fields such as industry, household appliances, communication, education, automotive electronics, medical treatment, home services, transportation trips, and consumer electronics in the next ten years [16].The key technology of intelligent music software is intelligent speech recognition, which is a branch of pattern recognition and belongs to the field of signal processing. Furthermore, it is linked with phonetics, linguistics, mathematical statistics, and neuroscience [17]. Intelligent voice recognition refers to the technology that allows a computer to take, interpret, and understand a person’s language information and automatically convert that information or command into the appropriate information or command [18]. The basic framework of intelligent speech recognition is shown in Figure 1.Figure 1
The basic framework of intelligent speech recognition.Unknown speech is transformed into an electrical signal by a microphone and then added to the input end of the recognition system. After pretreatment, the speech model is created based on human speech characteristics, the input speech signal is analyzed, the required features are extracted, and the speech recognition template is created on this basis. According to the speech recognition model, the computer should compare the features of the speech template stored in the computer with the input speech signal and find a sequence of ideal templates matching the input speech using specified search and matching algorithms. The computer’s identification results can then be delivered by looking up the table, according to the definition of this template [16].The acoustic model is one of the most important parts of the speech recognition system. The hidden Markov model (HMM) is widely used in the current mainstream for modeling. HMM has a solid statistical foundation and efficient learning methods, allowing it to learn directly from raw sequence data. It can handle variable-length inputs and uses locally learnable algorithms to address insertion and deletion penalties consistently. The current common recognition criterion for speech recognition is the maximum a posteriori (MAP) decoding rule [17, 18]. The acoustic observation vector sequence X is input, and the recognizer gives a word sequence W that maximizes the corresponding posterior probability.(1)W=argmaxWPWX=argmaxWPX/WPX,(2)PX=∑W′PX/W′PW′.SincePX is constant for the fixed input, the molecular values in equation (1) are mainly compared in the identification process, that is, the relative scores of different word sequences are compared. The word sequence W output by the traditional recognizer is as follows.(3)W=argmaxWPXWPW,where PX/W and PX/W′ are acoustic model scores, PW and PW′ represent the language model probability, and PX is the probability of X possibly occurring.


### 2.2. Soundscape Design

Musical soundscape design focuses on the beauty of music, the external beauty and grandeur of the music, and the inner sadness, joy, and sublime of music. The general soundscape design can be applied to squares, communities, parks, gardens, sites, and other places, mainly to test, improve, and design its environment [19, 20]. The design of the music soundscape is based on music melody, emphasizing music appreciation, music venues, music performances, and more music products. Music soundscape design is not the same as natural musical objects. The original natural music objects are created to be pushed to a higher level through the change of environment and sound, as well as the examination of the auditory system. It enriches and diversifies the musical landscape [21].According to Schafer’s theory of soundscape [22], the concept of soundscape is divided into three elements: listeners, sound, and environment. The listener’s hearing system is objectively existing and does not change in a short time as a result of these three factors: environment, sound, and listener. In general, the background sound cannot be significantly changed in a short period, but the signal and marker sounds can be changed by various sound generating devices, so they can be altered to some extent. Listeners can change the location and environment, as well as develop relevant infrastructure, to change the soundscape at any time [23]. Therefore, the relationship among the three can be represented as shown in Figure 2.Figure 2
Variable relation of three elements of the soundscape.The design variable is the most important variable in the soundscape environment, followed by the sound variable, and the auditory system is primarily the subsequent sensation and subjective evaluation criteria. Music soundscape design mainly involves the following aspects: music theme and music elements; the choice of music; selection of musical instruments and musical devices; sound elimination and expansion design; and regional environmental selection and audience analysis [24].


### 2.3. Theoretical Models of Music-Induced Emotion

#### 2.3.1. Music Cue Consistency Model

This model can explain the transfer of music data in great detail. It is thought that music works can establish resonance between the performer and the audience and that the listener’s music coding information symbol standard must be consistent with the performance and the author. The process of music emotion involves four aspects: the author and the performer, different music elements, the audience, and the external environment [25]. The specific relationship is given in Figure 3.Figure 3
Four factors inducing musical emotion.The four elements in Figure3 and the interaction among them are indispensable in explaining music-induced emotions. These four elements also greatly illustrate the complexity of music emotion itself and the difficulty of controlling variables.


#### 2.3.2. Music Expectation Model

Huron [26] pointed out a complete model of expectation theory. Huron believes that there are five reaction stages related to expectation in the process of music inducing individual emotional experience, including imaginative response, tension response, predictive response, reflective response, and evaluative response. Huron believes that the complete music experience is composed of emotional reactions in various periods, so music emotion is usually complex and multi-component.


#### 2.3.3. The Theoretical Model of Music Emotion Synergy

When individuals listen to music, they will follow the rhythm, melody contour, and other music information to make explicit behavioral responses, such as beating with hands, body dancing with the rhythm, and humming melody. At present, the synergy theory can explain the basis for the commonness of musical emotion with action synergy as the explicit behavior [27].


#### 2.3.4. Multiple Mechanism Theory Model

This model integrates previous studies and summarizes the mechanisms of music emotion induction from different dimensions, including brainstem reflex, evaluative conditioning reflex, visual association, situational event memory, music expectation, and rhythm fusion [28].


### 2.4. Positive and Negative Emotions

From the emotional valence level, from positive to negative, pleasure is gradually reduced [29]. Positive emotion is at the end of positive valence. On the contrary, negative emotion is at the end of negative valence. Positive emotions tend to produce a pleasant emotional experience, while negative emotions tend to produce unpleasant emotional experiences. When the objective facts or external environment meet the expectations and needs of the individual, it can induce positive emotions, and conversely, it can produce negative emotions [30]. Common adjectives of positive and negative emotions are shown in Table 1.Table 1
Adjectives of positive and negative emotions.
Positive emotionsNegative emotions1. Interested1. Worry2. Highly energetic2. Distracted3. Industrious3. Guilty4. Enthusiastic4. Fearful5. Proud5. Hostile6. Highly alert6. Vulnerable7. Encouraged7. Shy8. Firm8. Tense9. Focused9. Restless10. Dynamic10. Terrified


### 2.5. Experimental Design

In this study, 50 students from non-music majors and 50 students from music majors were recruited. They have no hearing impairment and no history of neurological or psychological diseases. All subjects in the experiment did not participate in similar experiments.First, a Chinese classical music major student was asked to play a positive emotion of Chinese classical music (Spring Snow), which is called classical music 1 in the follow-up discussion of this study. After playing, 50 non-music majors and 50 music majors were invited to fill in the emotional survey scale. In the next week, the Chinese classical music professional students used intelligent music software and learned and understood the repertoire on a deeper level with the help of soundscape. At the end of a week, the students majoring in Chinese classical music were asked to play the repertoire again. After the performance, 50 volunteers from non-music majors and 50 volunteers from music majors were asked to fill in the emotion survey scale. The specific experimental process is shown in Figure4.Figure 4
Flowchart of emotional experience investigation experiment.The same process of negative emotions of Chinese classical music (Eighteen Songs of a Nomad Flute) was carried out; in this study, the subsequent discussion is called classical music 2. The experiment was conducted from 9 : 00 AM to 10 : 00 AM every day. The experiment was scored on a 9-point system as shown in Table2.Table 2
Corresponding emotional score.
The intensity of emotional experienceScoreVery weak emotional experience1Weak emotional experience2–4General emotional experience5Strong emotional experience6–8Very strong emotional experience9The high score of positive emotional music represents the individual’s happy emotional state, and the lower the score is, the more indifferent is the individual. A high score of negative emotion indicates that individuals are in negative, sad, painful, and sad emotional state. The lower the score of negative emotion is, the more stable is the individual. Score statistics are made on the emotion survey scale filled in by the subjects, and MS Excel software is used to analyze and process the data.


## 2.1. Intelligent Network

IN is a kind of superposition network introduced in the traditional mobile exchange network to provide value-added services more flexibly, conveniently, economically, and effectively. The whole mobile network forms a network structure composed of the original exchange layer and the superposed intelligent layer. The exchange layer is responsible for completing the basic service call connection function, while some more complex value-added services have the intelligent layer for control realization [15]. The separation of the exchange and control functions is the most important feature of IN. Intelligent speech recognition technology has made significant progress in recent years, and it is expected that speech recognition technology will enter various fields such as industry, household appliances, communication, education, automotive electronics, medical treatment, home services, transportation trips, and consumer electronics in the next ten years [16].The key technology of intelligent music software is intelligent speech recognition, which is a branch of pattern recognition and belongs to the field of signal processing. Furthermore, it is linked with phonetics, linguistics, mathematical statistics, and neuroscience [17]. Intelligent voice recognition refers to the technology that allows a computer to take, interpret, and understand a person’s language information and automatically convert that information or command into the appropriate information or command [18]. The basic framework of intelligent speech recognition is shown in Figure 1.Figure 1
The basic framework of intelligent speech recognition.Unknown speech is transformed into an electrical signal by a microphone and then added to the input end of the recognition system. After pretreatment, the speech model is created based on human speech characteristics, the input speech signal is analyzed, the required features are extracted, and the speech recognition template is created on this basis. According to the speech recognition model, the computer should compare the features of the speech template stored in the computer with the input speech signal and find a sequence of ideal templates matching the input speech using specified search and matching algorithms. The computer’s identification results can then be delivered by looking up the table, according to the definition of this template [16].The acoustic model is one of the most important parts of the speech recognition system. The hidden Markov model (HMM) is widely used in the current mainstream for modeling. HMM has a solid statistical foundation and efficient learning methods, allowing it to learn directly from raw sequence data. It can handle variable-length inputs and uses locally learnable algorithms to address insertion and deletion penalties consistently. The current common recognition criterion for speech recognition is the maximum a posteriori (MAP) decoding rule [17, 18]. The acoustic observation vector sequence X is input, and the recognizer gives a word sequence W that maximizes the corresponding posterior probability.(1)W=argmaxWPWX=argmaxWPX/WPX,(2)PX=∑W′PX/W′PW′.SincePX is constant for the fixed input, the molecular values in equation (1) are mainly compared in the identification process, that is, the relative scores of different word sequences are compared. The word sequence W output by the traditional recognizer is as follows.(3)W=argmaxWPXWPW,where PX/W and PX/W′ are acoustic model scores, PW and PW′ represent the language model probability, and PX is the probability of X possibly occurring.


## 2.2. Soundscape Design

Musical soundscape design focuses on the beauty of music, the external beauty and grandeur of the music, and the inner sadness, joy, and sublime of music. The general soundscape design can be applied to squares, communities, parks, gardens, sites, and other places, mainly to test, improve, and design its environment [19, 20]. The design of the music soundscape is based on music melody, emphasizing music appreciation, music venues, music performances, and more music products. Music soundscape design is not the same as natural musical objects. The original natural music objects are created to be pushed to a higher level through the change of environment and sound, as well as the examination of the auditory system. It enriches and diversifies the musical landscape [21].According to Schafer’s theory of soundscape [22], the concept of soundscape is divided into three elements: listeners, sound, and environment. The listener’s hearing system is objectively existing and does not change in a short time as a result of these three factors: environment, sound, and listener. In general, the background sound cannot be significantly changed in a short period, but the signal and marker sounds can be changed by various sound generating devices, so they can be altered to some extent. Listeners can change the location and environment, as well as develop relevant infrastructure, to change the soundscape at any time [23]. Therefore, the relationship among the three can be represented as shown in Figure 2.Figure 2
Variable relation of three elements of the soundscape.The design variable is the most important variable in the soundscape environment, followed by the sound variable, and the auditory system is primarily the subsequent sensation and subjective evaluation criteria. Music soundscape design mainly involves the following aspects: music theme and music elements; the choice of music; selection of musical instruments and musical devices; sound elimination and expansion design; and regional environmental selection and audience analysis [24].


## 2.3. Theoretical Models of Music-Induced Emotion

### 2.3.1. Music Cue Consistency Model

This model can explain the transfer of music data in great detail. It is thought that music works can establish resonance between the performer and the audience and that the listener’s music coding information symbol standard must be consistent with the performance and the author. The process of music emotion involves four aspects: the author and the performer, different music elements, the audience, and the external environment [25]. The specific relationship is given in Figure 3.Figure 3
Four factors inducing musical emotion.The four elements in Figure3 and the interaction among them are indispensable in explaining music-induced emotions. These four elements also greatly illustrate the complexity of music emotion itself and the difficulty of controlling variables.


### 2.3.2. Music Expectation Model

Huron [26] pointed out a complete model of expectation theory. Huron believes that there are five reaction stages related to expectation in the process of music inducing individual emotional experience, including imaginative response, tension response, predictive response, reflective response, and evaluative response. Huron believes that the complete music experience is composed of emotional reactions in various periods, so music emotion is usually complex and multi-component.


### 2.3.3. The Theoretical Model of Music Emotion Synergy

When individuals listen to music, they will follow the rhythm, melody contour, and other music information to make explicit behavioral responses, such as beating with hands, body dancing with the rhythm, and humming melody. At present, the synergy theory can explain the basis for the commonness of musical emotion with action synergy as the explicit behavior [27].


### 2.3.4. Multiple Mechanism Theory Model

This model integrates previous studies and summarizes the mechanisms of music emotion induction from different dimensions, including brainstem reflex, evaluative conditioning reflex, visual association, situational event memory, music expectation, and rhythm fusion [28].


## 2.3.1. Music Cue Consistency Model

This model can explain the transfer of music data in great detail. It is thought that music works can establish resonance between the performer and the audience and that the listener’s music coding information symbol standard must be consistent with the performance and the author. The process of music emotion involves four aspects: the author and the performer, different music elements, the audience, and the external environment [25]. The specific relationship is given in Figure 3.Figure 3
Four factors inducing musical emotion.The four elements in Figure3 and the interaction among them are indispensable in explaining music-induced emotions. These four elements also greatly illustrate the complexity of music emotion itself and the difficulty of controlling variables.


## 2.3.2. Music Expectation Model

Huron [26] pointed out a complete model of expectation theory. Huron believes that there are five reaction stages related to expectation in the process of music inducing individual emotional experience, including imaginative response, tension response, predictive response, reflective response, and evaluative response. Huron believes that the complete music experience is composed of emotional reactions in various periods, so music emotion is usually complex and multi-component.


## 2.3.3. The Theoretical Model of Music Emotion Synergy

When individuals listen to music, they will follow the rhythm, melody contour, and other music information to make explicit behavioral responses, such as beating with hands, body dancing with the rhythm, and humming melody. At present, the synergy theory can explain the basis for the commonness of musical emotion with action synergy as the explicit behavior [27].


## 2.3.4. Multiple Mechanism Theory Model

This model integrates previous studies and summarizes the mechanisms of music emotion induction from different dimensions, including brainstem reflex, evaluative conditioning reflex, visual association, situational event memory, music expectation, and rhythm fusion [28].


## 2.4. Positive and Negative Emotions

From the emotional valence level, from positive to negative, pleasure is gradually reduced [29]. Positive emotion is at the end of positive valence. On the contrary, negative emotion is at the end of negative valence. Positive emotions tend to produce a pleasant emotional experience, while negative emotions tend to produce unpleasant emotional experiences. When the objective facts or external environment meet the expectations and needs of the individual, it can induce positive emotions, and conversely, it can produce negative emotions [30]. Common adjectives of positive and negative emotions are shown in Table 1.Table 1
Adjectives of positive and negative emotions.
Positive emotionsNegative emotions1. Interested1. Worry2. Highly energetic2. Distracted3. Industrious3. Guilty4. Enthusiastic4. Fearful5. Proud5. Hostile6. Highly alert6. Vulnerable7. Encouraged7. Shy8. Firm8. Tense9. Focused9. Restless10. Dynamic10. Terrified


## 2.5. Experimental Design

In this study, 50 students from non-music majors and 50 students from music majors were recruited. They have no hearing impairment and no history of neurological or psychological diseases. All subjects in the experiment did not participate in similar experiments.First, a Chinese classical music major student was asked to play a positive emotion of Chinese classical music (Spring Snow), which is called classical music 1 in the follow-up discussion of this study. After playing, 50 non-music majors and 50 music majors were invited to fill in the emotional survey scale. In the next week, the Chinese classical music professional students used intelligent music software and learned and understood the repertoire on a deeper level with the help of soundscape. At the end of a week, the students majoring in Chinese classical music were asked to play the repertoire again. After the performance, 50 volunteers from non-music majors and 50 volunteers from music majors were asked to fill in the emotion survey scale. The specific experimental process is shown in Figure4.Figure 4
Flowchart of emotional experience investigation experiment.The same process of negative emotions of Chinese classical music (Eighteen Songs of a Nomad Flute) was carried out; in this study, the subsequent discussion is called classical music 2. The experiment was conducted from 9 : 00 AM to 10 : 00 AM every day. The experiment was scored on a 9-point system as shown in Table2.Table 2
Corresponding emotional score.
The intensity of emotional experienceScoreVery weak emotional experience1Weak emotional experience2–4General emotional experience5Strong emotional experience6–8Very strong emotional experience9The high score of positive emotional music represents the individual’s happy emotional state, and the lower the score is, the more indifferent is the individual. A high score of negative emotion indicates that individuals are in negative, sad, painful, and sad emotional state. The lower the score of negative emotion is, the more stable is the individual. Score statistics are made on the emotion survey scale filled in by the subjects, and MS Excel software is used to analyze and process the data.


## 3. Results and Discussion

### 3.1. Statistical Results of Positive Emotion Classical Music Score

In the work of positive emotion classical music learning effect, the first score is recorded as Score 1, and the second score is recorded as Score 2. After sorting out the positive emotion survey scale, the scores of students majoring in music and non-music were obtained. The results for positive and negative emotions in classical music scores are shown in Figures5 and 6, respectively. The abscissa represents the sample serial number, and the ordinate represents the score.Figure 5
Scores of music majors in positive emotion classical music experiment.Figure 6
Scores of non-music majors in positive emotion classical music experiment.After listening to classical music 1 for the first time, the emotional experience of 50 students in music majors is weak, and the scores are mainly concentrated in 2–5 points, with an average of 3.64 points. Only a few students have a strong emotional experience, reaching 6 points. After listening to classical music 1 for the second time, the emotional experience is enhanced. Most students scored about 7 points, which shows that the emotional experience is strong, and only a few students scored about 4 points and 9 points, indicating that the number of students with weak emotional experience and very strong emotional experience is relatively small. However, from the overall trend, the second score is higher than the first score. This shows that the emotional experience is enhanced, which also suggests that the player’s learning effect is obvious, and the music played can better move people into the music atmosphere.After listening to classical music 1 for the first time, the average score of the emotional experience of 50 students in the non-music majors is 3.02, which is weaker than that of music majors. Moreover, only a few students have a general emotional experience, which can reach 5 points. After listening to classical music 1 for the second time, the emotional experience of most is distinctly enhanced, and the score is enhanced up to 7 points. Only seven students scored below 4 points, and five students scored 9 points. Compared with students majoring in music, it is found that the score in the non-music major is low, but in general, Score 2 is superior to Score 1, which also proves that the player’s learning effect is distinct.


### 3.2. Statistical Results of Classical Music Score of Negative Emotions

In the investigation experiment of learning effect of negative emotion classical music, similarly, the first score is recorded as Score 1, and the second score is recorded as Score 2. After finishing the negative emotion questionnaire, the scores of students majoring in music and non-music were obtained. The results of negative emotions are shown in Figures7 and 8, respectively, and the radial axis represents the score.Figure 7
Scores of music majors in negative emotion classical music experiment.Figure 8
Scores of non-music majors in negative emotion classical music experiment.Generally, the students majoring in music have a weak emotional experience in classical music 2, and the scores are concentrated in the range of 2–4 points. A few students have a strong emotional experience, which can reach 6 points. After the second performance, scores increase, and emotional experience enhances, and a few students’ scores can reach 5-6 points. The overall comparison of the two scores reveals that the second score is mostly greater than the first score, which shows that after a week of learning, the player has progressed in the exploration of the music, and the ability to bring emotions is improved.Non-music majors’ emotional experience for classical music 2 is weak for the first time, and the score is concentrated between 1 and 4 points. After listening to classical music 2 for the second time, more than 90% of the students’ emotional experience is enhanced, and only three students’ scores remained the same. In general, in contrast with music majors, the intensity of emotional experience increases less, which may be related to the music foundation of music majors.The use of intelligent music software and aid of soundscape have a certain effect on learning Chinese classical music, and the emotional experience of the audience is enhanced apparently, which provides a new method for students to learn Chinese classical music. This new learning model can not only make students better integrate into the world of music but also improve students’ enthusiasm for learning and deepen students’ understanding of Chinese classical music to express music more accurately in performance.


## 3.1. Statistical Results of Positive Emotion Classical Music Score

In the work of positive emotion classical music learning effect, the first score is recorded as Score 1, and the second score is recorded as Score 2. After sorting out the positive emotion survey scale, the scores of students majoring in music and non-music were obtained. The results for positive and negative emotions in classical music scores are shown in Figures5 and 6, respectively. The abscissa represents the sample serial number, and the ordinate represents the score.Figure 5
Scores of music majors in positive emotion classical music experiment.Figure 6
Scores of non-music majors in positive emotion classical music experiment.After listening to classical music 1 for the first time, the emotional experience of 50 students in music majors is weak, and the scores are mainly concentrated in 2–5 points, with an average of 3.64 points. Only a few students have a strong emotional experience, reaching 6 points. After listening to classical music 1 for the second time, the emotional experience is enhanced. Most students scored about 7 points, which shows that the emotional experience is strong, and only a few students scored about 4 points and 9 points, indicating that the number of students with weak emotional experience and very strong emotional experience is relatively small. However, from the overall trend, the second score is higher than the first score. This shows that the emotional experience is enhanced, which also suggests that the player’s learning effect is obvious, and the music played can better move people into the music atmosphere.After listening to classical music 1 for the first time, the average score of the emotional experience of 50 students in the non-music majors is 3.02, which is weaker than that of music majors. Moreover, only a few students have a general emotional experience, which can reach 5 points. After listening to classical music 1 for the second time, the emotional experience of most is distinctly enhanced, and the score is enhanced up to 7 points. Only seven students scored below 4 points, and five students scored 9 points. Compared with students majoring in music, it is found that the score in the non-music major is low, but in general, Score 2 is superior to Score 1, which also proves that the player’s learning effect is distinct.


## 3.2. Statistical Results of Classical Music Score of Negative Emotions

In the investigation experiment of learning effect of negative emotion classical music, similarly, the first score is recorded as Score 1, and the second score is recorded as Score 2. After finishing the negative emotion questionnaire, the scores of students majoring in music and non-music were obtained. The results of negative emotions are shown in Figures7 and 8, respectively, and the radial axis represents the score.Figure 7
Scores of music majors in negative emotion classical music experiment.Figure 8
Scores of non-music majors in negative emotion classical music experiment.Generally, the students majoring in music have a weak emotional experience in classical music 2, and the scores are concentrated in the range of 2–4 points. A few students have a strong emotional experience, which can reach 6 points. After the second performance, scores increase, and emotional experience enhances, and a few students’ scores can reach 5-6 points. The overall comparison of the two scores reveals that the second score is mostly greater than the first score, which shows that after a week of learning, the player has progressed in the exploration of the music, and the ability to bring emotions is improved.Non-music majors’ emotional experience for classical music 2 is weak for the first time, and the score is concentrated between 1 and 4 points. After listening to classical music 2 for the second time, more than 90% of the students’ emotional experience is enhanced, and only three students’ scores remained the same. In general, in contrast with music majors, the intensity of emotional experience increases less, which may be related to the music foundation of music majors.The use of intelligent music software and aid of soundscape have a certain effect on learning Chinese classical music, and the emotional experience of the audience is enhanced apparently, which provides a new method for students to learn Chinese classical music. This new learning model can not only make students better integrate into the world of music but also improve students’ enthusiasm for learning and deepen students’ understanding of Chinese classical music to express music more accurately in performance.


## 4. Conclusion

A soundscape is a sonic environment in which auditory perception and social or cultural understanding coexist. Artificial intelligence is gradually being applied to the world of music using IN. This study used intelligent music software to learn Chinese classical music and used soundscape to investigate the learning effect of this new mode. The intensity of students’ emotional experiences is used to assess the learning effect of Chinese classical music, and the positive and negative emotional impacts of Chinese classical music on learning are investigated. To examine the emotional experience of players in playing before and after learning, 50 music majors and 50 non-music majors were selected. It is was observed that in the positive emotional classical music experiment, most music majors and non-music majors change from weak emotional experience (2–5 points) to strong emotional experience (about 7 points). The comparison between non-music majors and music majors indicated that the overall emotional experience score is low. In the negative emotion classical music experiment, most music majors and non-music majors enhanced their emotional experience, and a small number of music majors increased their scores, finally reaching 5-6 points. Overall, the second score was higher than the first score, namely, emotional experience enhances, which also suggests that the player’s learning effect is obvious, and the music can better move people into the music atmosphere. The major shortcoming of this study is that the survey results of emotional experience have certain subjectivity, which affects the experimental results. Future work will focus on exploring other music learning impact evaluation indices.



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*Source: 2085413-2022-05-13.xml*