# Design of Music Teaching System Based on Internet of Things Multimedia Intelligent Platform

Authors: Bin Xie
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to improve the practical and popularization value of the multimedia vocal music teaching system, the author proposes a teaching system based on the Internet of Things multimedia intelligent platform. Mainly use Visual C++ to realize the acquisition, playback, and display of audio and realize the real-time modification of the sound wave waveform on the computer and also add the function of vocal score. Experimental results show that in the pitch comparison, the standard fundamental frequency average value of the fundamental frequency track of the two pieces of music is obtained by the cepstral method:avgF0=143.12HZ and the average fundamental frequency of the trial singing: avgF0=142.05HZ. The average distance and score of each parameter of the testers are mindisv=726.126 for pitch intensity, pathlength=144; mindisp=4.51987, pathlength=163 for pitch, breathsmoothness=484.20. Conclusion. This method not only improves the intuitiveness and interaction of vocal music teaching, but also increases the interest of vocal music teaching.


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## Body

## 1. Introduction

Since entering the twenty-first century, the popularization of computer technology has reached an unprecedented speed, and people’s life has gradually become inseparable from computer technology. The information technology and digital technology it brings have fully covered our work and life. The same is true in the field of music; digital technology to assist music production and music editing has become a very common phenomenon in the music industry, and several famous software companies in the world have launched very practical music production software; the CD we hear now, MP3, and other music are produced through these software [1]. Music production software can help music producers set and edit various timbres, volume, pitch, rhythm, etc., and finally produce excellent musical sounds. In China’s digital multimedia technology-assisted music teaching, in recent years, it has also achieved considerable development; all kinds of large, medium, and small schools in China have also started to use multimedia technology to assist the classroom teaching of music. The courses of music appreciation, music theory, music composition, and music production are the most common places to use multimedia teaching; after the use of multimedia-assisted teaching, these courses have achieved good teaching results. It improves the quality and efficiency of music teaching, expands the popularization of music teaching, and is loved by the taught group. However, the introduction of digital technology into music teaching has also encountered many problems; the most important ones are focusing only on the use of multimedia technology while ignoring the use of other advantages of digital technology. The status quo of music teaching is that most schools in China focus on using multimedia digital technology to teach music appreciation courses and basic theory courses [2, 3]. Convenient and convenient multimedia technology is only a technical means in digital technology, and it can only be undertaken in music teaching, the teaching work of this part of popularizing music and improving the efficiency of music teaching, its use does not maximize the advantages of digital technology-assisted music teaching, and it does not well reflect the scientific, intuitive, interactive, and visual advantages of digital technology. In view of the current situation of music education in China, this subject wants to conduct in-depth research on digital technology to assist music teaching research and contribute to the modernization of music teaching. Because the scope of music education is relatively broad, the author can only make breakthroughs from a small aspect, hoping to help digital technology gradually, introduced to all aspects of music education.


## 2. Literature Review

Guo and Liu discussed the new connotation and new characteristics of online learning resources, focusing on what kind of learning resources Internet + education needs and how to realize the sharing of high-quality resources in the Internet + era [4]; finally, China’s Internet + resource strategy and its implementation path are proposed, which have certain theoretical value and strong practical guiding significance. Pradhan et al. have successively discussed the quality requirements of vocal music teachers and the exertion of students’ subjectivity, the language of vocal music classroom teaching and its normative application, the core content of vocal music teaching—the teaching of key professional skills, and the teaching of vocal music singing skills; research and analysis are carried out in turn in terms of thinking about contradictions [5]. Zhang et al. advocated teaching design at different levels according to the different characteristics of different students and stratified teaching according to their aptitude, and reforming and innovating education and teaching, reflecting the development direction of art education in the new era [6]. Liu enumerated the landmark achievements in the use and development of new media software. Try to use the mobile Internet analysis chart to interpret the development process of new media, explore the internal driving force of new media development, and have positive guiding significance for the application of new media [7]. Wang et al., through case studies and comparative research methods, mainly discussed the reform methods of traditional teaching such as “learning by listening,” “learning by doing,” and “learning by feeling,” and different online learning and teaching methods, etc. [8] enable teachers to help students develop the knowledge and skills they need in the digital age; the purpose is to guide students to cultivate and improve their thinking ability and knowledge level towards success. It aims to promote the transformation of teaching paradigms, implement effective teaching and learning, and improve the level of support services for teachers and the overall teaching quality. Zhao et al. provide a very procedural and unified teaching method that compromises the combination of scientific, mechanical, and holistic [9].The author proposes a design method for multimedia vocal music teaching and develops an interactive multimedia vocal music teaching system, which enables vocal music teaching to achieve more independent choices and human-computer interaction functions and lays the foundation for the autonomy of vocal music teaching [10]. At the same time, it also provides a new way for the realization of multimedia vocal music teaching.


## 3. Research Methods

### 3.1. Implementation of Interactive Multimedia Vocal Music Teaching System

The key to realizing the multimedia vocal music teaching system is the establishment of the vocal music teaching module. The vocal music teaching module is mainly based on professional vocal practice, and users can modify the singing results in real time and form the correct pronunciation, so as to realize the real interactive teaching mode [11]. At the same time, a vocal score function is also added. First, a singing model is established through the original singing audio, and the comparison parameters of volume, pitch, and breath are selected, and then the extracted singing audio parameters are compared with the original singing model parameters, and finally, the corresponding evaluation and opinions are given according to the matching degree of the two.


### 3.2. Establishment of Interactive Multimedia Vocal Music Teaching System

In order to realize the systematic multimedia teaching of vocal music, it is very important to establish a complete library of vocal music. Considering the wide adaptability of teaching content, the music library firstly divides the parts and vocal types, and users can choose the corresponding vocal music library for practice according to their own needs. The content of the music library refers to professional vocal music theory textbooks and practical guidance materials, and a vocal music practice tutorial is designed in a targeted manner. For example, the bel canto baritone course mainly includes breathing exercises, humming exercises (open and closed mouth), 5 vowel exercisesa，e，i，o，u, and scale exercises of changing voice areas, a total of 9 exercises. Another example, the national female voice course mainly focuses on breathing exercises and various humming exercises [12].The use of digital means should give full play to its advantages to make vocal music teaching more intuitive. Its approach is to record the audio passages sung by students in a targeted manner and help students find a good voice state by revising and comparing the results of several times of singing, so that students can feel how to mobilize their singing state is correct. For this purpose, the teaching interaction module established by the author has the following two functions: (1) audio collection and playback and (2) audio editing [13].In vocal music teaching, in order to let students sing a good voice, first of all, let students know their own voice. Let the students know what the voice they usually sing is like and is this kind of sound beautiful [14]. Only when students know their own voice can they find out their shortcomings and make improvements. In the vocal music teaching in multimedia mode, the function of audio capture and playback is very important and very practical, mainly using low-level audio functions to achieve.Low-level audio services can deal directly with audio drivers, operate on audio data, and perform special sound effects, including waveform audio and MIDI low-level functions. Figure1 illustrates the Windows audio services hierarchy.Figure 1
Hierarchical relationship of Windows audio services.The basic process of waveform audio acquisition is as follows:(1)
Use the functions waveInGetNumDevs() and waveInGetDevCps() to query whether the system has a device for recording sound, and check its performance(2)
Call the following function to open the sound input device, and return the handle hwi of the waveform input device for later use:MMRESULTwaveInOpen(LPHWAVEIN phwi, UINTuDeviceID, LPWAVEEFORMATEX pwfx, DWORD dwCallback, DWORD dwCallbackInstance, DWORD fdwOpen).Pwfx points to the WAVEFORMATEX data structure for waveform audio, which determines the format of the acquired waveform data. DwCallback points to a callback function that handles audio input.(3)
Before entering the audio record, a memory area needs to be defined to store the data [15]. Then call the function:MMRESULT waveInPrepareHeader(HWAVEIN hwi,LPWAVEHDR pwh, UINT cbwh). Where pwh points to a WAVEHDR structure, the data storage area location, size, and other fields in this structure must be copied and specified in advance. Then, call the function again:(4)
After the above function is called successfully, you can start recording sound and call the function: MMRESULT waveInStart(HWAVEIN hwi)(5)
After completing the route, call the following functions in turn to clear the waveform audio data structure WAVEHDR, release the allocated resources, and close the waveform audio input device:MMRESULT waveInUnprepareHeader(HWAVEN hwi, LPWAVEHDR pwh, UINT cbwh), MMRESULT waveInStop(HWAVEIN hwi), MMRESULT waveInClose(HWAVEIN hwi).The function called by using the low-level audio function to play the waveform sound is similar to the function called by the waveform audio acquisition, and the implementation process is also similar [16].In order to realize the interactive characteristics of digital vocal music teaching, this module adds an audio editing function. Students can modify the sound waveform and pitch parameters in real time and compare the sound effects before and after the modification through audio playback; in this way, students have a more intuitive and specific understanding of the singing state. The implementation method is as follows:(1)
Graphical display of sound. Set the time interval, and periodically take out the collected sound signal from the memory, the signal is a function of time, and a waveform curve is drawn(2)
Volume editing. The volume editor on the program interface, the program mainly uses the function Envelope to modify the amplitude envelope, where lpWaveData is the waveform audio data block pointer, factor is the editing factor, and the waveform amplitude can be changed by changing the value of factor(1)short∗Wave.lpWaveDatai=shortshort∗Wave.lpWaveDatai∗factor。(3)
Pitch editing. Vocal singing often encounters such a problem: In a specific sound area, the singer often causes intonation problems due to the position and breath of the voice [17]. This problem is difficult to solve, especially for students who have not yet been able to use their singing skills proficiently, and it is even more difficult to solve the intonation problem at this time, because in this state, the inner pitch of the singer is accurate and the immature singing technology causes the pitch difference. Without a reference, the singer is difficult to detect; in response to this situation, the teacher can cut out the waveform with inaccurate pitch, point out the problem first, then correct the pitch to establish the accuracy of hearing, and then tell the students that they should make subtle adjustments to the singing state, such as insufficient breath and open pharynx, not enough, and the sound position is low; this allows students to reach the pitch in a good singing state rather than a blunt “pitch enough.” This function mainly adopts the pitch shifter plug-in in Opensource’s FMODProgrammers API Win32, which realizes the real-time pitch modification and audio playback functionsAfter students practice vocal music, the system can give an objective evaluation based on the singing results. The key to the singing instruction module is to establish the corresponding vocal measurement method and scoring mechanism.Vocal scoring is different from previous voice scoring methods. Voice scoring generally uses a factor evaluation method; this system uses a vocal measurement method based on the comparison of audio feature parameters, by analyzing the sound wave parameters of the user’s audition voice and the original singing model; the characteristics of breath, sound intensity, and pitch are extracted; matching and comparison are carried out; and then the scoring mechanism gives objective evaluation scores according to the degree of similarity.Breath is an important aspect of the basic skills of singing; the quality of breath control during singing will directly affect the effect of vocalization and the performance of emotions. If you cannot master your breathing, you cannot sing a good voice and even damage your voice [18]. The key technique for singing breath is the length and smoothness of the breath. The system measures the continuity and smoothness of breath by calculating the standard deviation of the test sound waveform. Standard deviation is a measure of how far apart a set of values is from the mean. For a vector X, the function stdX of its standard deviation is the following formula:
(2)stdX=1n−1∑i=1nxi−x¯21/2.Among them,n represents the number of sampling points, and x¯ represents the average amplitude.The larger the calculated standard deviation, the less stable the breath; otherwise, the better the user’s grasp of the breath.The sound intensity indicates the volume of singing, and in vocal singing, it reflects the opening and closing amplitude of the vocal cords when they vibrate and the impact force of the breath. This system performs short-duration processing on the acoustic signal, the volume parameters are extracted by the frame-by-frame method, and the volume intensity curve is obtained. It is assumed that each frame of signal is represented bySnm, where m=0,1,⋯,M−1，n=0,1,⋯,N−1 and N are the total number of frames, that is, the length of the volume intensity curve, and M is the size of the sound frame. The volume intensity curve is defined as the following formula:
(3)Magn=1M∑m=0M−1Snm,n=0,1,⋯,N−1.In the process of singing, the singer’s grasp of the pitch is reflected in the accuracy of the singing pitch; this accuracy can not only measure the singer’s vocal skills, but also reflect his innate musical hearing and sense of music. The system measures the pitch accuracy by comparing the audition results with the spectrum of the original singing model, the specific implementation method is to first extract the short time frame of the signal to be compared, then extract the fundamental frequency parameters by the cepstrum (CEP-cepstrum) method, and finally use the dynamic time warping (DTW) method to perform data comparison; since DTW continuously calculates the distance between the two vectors to find the optimal matching path, the obtained matching between the two vectors is a regular function with the smallest cumulative distance; this ensures that there is a maximum acoustic similarity between them. Assuming that the test sound signal parameters shareI frame vector, and the reference template share J frame vector, and I≠J, then dynamic time warping is to find a time warping function j=wi, which nonlinearly maps the time axis i of the test vector to the time of the template on the axis j, and make the function w satisfy, as shown in the following formula:
(4)D=minxi∑i=1IdTi,Rwi.Among them,dTi,Rwi is the distance measure between the test vector Ti of the ith frame and the template vector Ri of the jth frame, and D is the distance between the two vectors in the case of optimal time warping.By transforming the sound intensity parameter and the pitch parameter through DTW, respectively, two minimum correction path paths and corresponding DTW distances can be obtained, these two distances reflect the difference in volume and melody of the two songs, respectively. The smaller the difference, the more similar the two are.


## 3.1. Implementation of Interactive Multimedia Vocal Music Teaching System

The key to realizing the multimedia vocal music teaching system is the establishment of the vocal music teaching module. The vocal music teaching module is mainly based on professional vocal practice, and users can modify the singing results in real time and form the correct pronunciation, so as to realize the real interactive teaching mode [11]. At the same time, a vocal score function is also added. First, a singing model is established through the original singing audio, and the comparison parameters of volume, pitch, and breath are selected, and then the extracted singing audio parameters are compared with the original singing model parameters, and finally, the corresponding evaluation and opinions are given according to the matching degree of the two.


## 3.2. Establishment of Interactive Multimedia Vocal Music Teaching System

In order to realize the systematic multimedia teaching of vocal music, it is very important to establish a complete library of vocal music. Considering the wide adaptability of teaching content, the music library firstly divides the parts and vocal types, and users can choose the corresponding vocal music library for practice according to their own needs. The content of the music library refers to professional vocal music theory textbooks and practical guidance materials, and a vocal music practice tutorial is designed in a targeted manner. For example, the bel canto baritone course mainly includes breathing exercises, humming exercises (open and closed mouth), 5 vowel exercisesa，e，i，o，u, and scale exercises of changing voice areas, a total of 9 exercises. Another example, the national female voice course mainly focuses on breathing exercises and various humming exercises [12].The use of digital means should give full play to its advantages to make vocal music teaching more intuitive. Its approach is to record the audio passages sung by students in a targeted manner and help students find a good voice state by revising and comparing the results of several times of singing, so that students can feel how to mobilize their singing state is correct. For this purpose, the teaching interaction module established by the author has the following two functions: (1) audio collection and playback and (2) audio editing [13].In vocal music teaching, in order to let students sing a good voice, first of all, let students know their own voice. Let the students know what the voice they usually sing is like and is this kind of sound beautiful [14]. Only when students know their own voice can they find out their shortcomings and make improvements. In the vocal music teaching in multimedia mode, the function of audio capture and playback is very important and very practical, mainly using low-level audio functions to achieve.Low-level audio services can deal directly with audio drivers, operate on audio data, and perform special sound effects, including waveform audio and MIDI low-level functions. Figure1 illustrates the Windows audio services hierarchy.Figure 1
Hierarchical relationship of Windows audio services.The basic process of waveform audio acquisition is as follows:(1)
Use the functions waveInGetNumDevs() and waveInGetDevCps() to query whether the system has a device for recording sound, and check its performance(2)
Call the following function to open the sound input device, and return the handle hwi of the waveform input device for later use:MMRESULTwaveInOpen(LPHWAVEIN phwi, UINTuDeviceID, LPWAVEEFORMATEX pwfx, DWORD dwCallback, DWORD dwCallbackInstance, DWORD fdwOpen).Pwfx points to the WAVEFORMATEX data structure for waveform audio, which determines the format of the acquired waveform data. DwCallback points to a callback function that handles audio input.(3)
Before entering the audio record, a memory area needs to be defined to store the data [15]. Then call the function:MMRESULT waveInPrepareHeader(HWAVEIN hwi,LPWAVEHDR pwh, UINT cbwh). Where pwh points to a WAVEHDR structure, the data storage area location, size, and other fields in this structure must be copied and specified in advance. Then, call the function again:(4)
After the above function is called successfully, you can start recording sound and call the function: MMRESULT waveInStart(HWAVEIN hwi)(5)
After completing the route, call the following functions in turn to clear the waveform audio data structure WAVEHDR, release the allocated resources, and close the waveform audio input device:MMRESULT waveInUnprepareHeader(HWAVEN hwi, LPWAVEHDR pwh, UINT cbwh), MMRESULT waveInStop(HWAVEIN hwi), MMRESULT waveInClose(HWAVEIN hwi).The function called by using the low-level audio function to play the waveform sound is similar to the function called by the waveform audio acquisition, and the implementation process is also similar [16].In order to realize the interactive characteristics of digital vocal music teaching, this module adds an audio editing function. Students can modify the sound waveform and pitch parameters in real time and compare the sound effects before and after the modification through audio playback; in this way, students have a more intuitive and specific understanding of the singing state. The implementation method is as follows:(1)
Graphical display of sound. Set the time interval, and periodically take out the collected sound signal from the memory, the signal is a function of time, and a waveform curve is drawn(2)
Volume editing. The volume editor on the program interface, the program mainly uses the function Envelope to modify the amplitude envelope, where lpWaveData is the waveform audio data block pointer, factor is the editing factor, and the waveform amplitude can be changed by changing the value of factor(1)short∗Wave.lpWaveDatai=shortshort∗Wave.lpWaveDatai∗factor。(3)
Pitch editing. Vocal singing often encounters such a problem: In a specific sound area, the singer often causes intonation problems due to the position and breath of the voice [17]. This problem is difficult to solve, especially for students who have not yet been able to use their singing skills proficiently, and it is even more difficult to solve the intonation problem at this time, because in this state, the inner pitch of the singer is accurate and the immature singing technology causes the pitch difference. Without a reference, the singer is difficult to detect; in response to this situation, the teacher can cut out the waveform with inaccurate pitch, point out the problem first, then correct the pitch to establish the accuracy of hearing, and then tell the students that they should make subtle adjustments to the singing state, such as insufficient breath and open pharynx, not enough, and the sound position is low; this allows students to reach the pitch in a good singing state rather than a blunt “pitch enough.” This function mainly adopts the pitch shifter plug-in in Opensource’s FMODProgrammers API Win32, which realizes the real-time pitch modification and audio playback functionsAfter students practice vocal music, the system can give an objective evaluation based on the singing results. The key to the singing instruction module is to establish the corresponding vocal measurement method and scoring mechanism.Vocal scoring is different from previous voice scoring methods. Voice scoring generally uses a factor evaluation method; this system uses a vocal measurement method based on the comparison of audio feature parameters, by analyzing the sound wave parameters of the user’s audition voice and the original singing model; the characteristics of breath, sound intensity, and pitch are extracted; matching and comparison are carried out; and then the scoring mechanism gives objective evaluation scores according to the degree of similarity.Breath is an important aspect of the basic skills of singing; the quality of breath control during singing will directly affect the effect of vocalization and the performance of emotions. If you cannot master your breathing, you cannot sing a good voice and even damage your voice [18]. The key technique for singing breath is the length and smoothness of the breath. The system measures the continuity and smoothness of breath by calculating the standard deviation of the test sound waveform. Standard deviation is a measure of how far apart a set of values is from the mean. For a vector X, the function stdX of its standard deviation is the following formula:
(2)stdX=1n−1∑i=1nxi−x¯21/2.Among them,n represents the number of sampling points, and x¯ represents the average amplitude.The larger the calculated standard deviation, the less stable the breath; otherwise, the better the user’s grasp of the breath.The sound intensity indicates the volume of singing, and in vocal singing, it reflects the opening and closing amplitude of the vocal cords when they vibrate and the impact force of the breath. This system performs short-duration processing on the acoustic signal, the volume parameters are extracted by the frame-by-frame method, and the volume intensity curve is obtained. It is assumed that each frame of signal is represented bySnm, where m=0,1,⋯,M−1，n=0,1,⋯,N−1 and N are the total number of frames, that is, the length of the volume intensity curve, and M is the size of the sound frame. The volume intensity curve is defined as the following formula:
(3)Magn=1M∑m=0M−1Snm,n=0,1,⋯,N−1.In the process of singing, the singer’s grasp of the pitch is reflected in the accuracy of the singing pitch; this accuracy can not only measure the singer’s vocal skills, but also reflect his innate musical hearing and sense of music. The system measures the pitch accuracy by comparing the audition results with the spectrum of the original singing model, the specific implementation method is to first extract the short time frame of the signal to be compared, then extract the fundamental frequency parameters by the cepstrum (CEP-cepstrum) method, and finally use the dynamic time warping (DTW) method to perform data comparison; since DTW continuously calculates the distance between the two vectors to find the optimal matching path, the obtained matching between the two vectors is a regular function with the smallest cumulative distance; this ensures that there is a maximum acoustic similarity between them. Assuming that the test sound signal parameters shareI frame vector, and the reference template share J frame vector, and I≠J, then dynamic time warping is to find a time warping function j=wi, which nonlinearly maps the time axis i of the test vector to the time of the template on the axis j, and make the function w satisfy, as shown in the following formula:
(4)D=minxi∑i=1IdTi,Rwi.Among them,dTi,Rwi is the distance measure between the test vector Ti of the ith frame and the template vector Ri of the jth frame, and D is the distance between the two vectors in the case of optimal time warping.By transforming the sound intensity parameter and the pitch parameter through DTW, respectively, two minimum correction path paths and corresponding DTW distances can be obtained, these two distances reflect the difference in volume and melody of the two songs, respectively. The smaller the difference, the more similar the two are.


## 4. Analysis of Results

### 4.1. Scoring Test Results

The scoring mechanism of the singing scoring system is based on the comparison of the above-mentioned breath, sound intensity, and pitch characteristics; the auditioner can see not only the scores in real time, but also the final total score of the audition. The system scoring formula is shown in the following:(5)score=k11001+a1mindisib1+k21001+a2mindispb2+k31001+a3stb3.Among them,a1,a2,a1,b1,b2,b3>0, k1+k2+k3=1, and k1, k2, k3 are the weights of each scoring parameter in the scoring mechanism, and mindisv and mindisp are the distances calculated by the sound intensity and pitch parameters, respectively. st represents the breath stability parameter; through repeated experiments of machine scoring and manual scoring, the weights reflect the best mapping relationship between the two, so that the computer can better simulate the expert scoring. The following is an example of a simulation experiment conducted with the male voice e etude:The measurement of breath is a process of self-comparison. In the experiment, the smoothness of breath is measured by calculating the standard deviation of the test sound waveform. In the sound intensity comparison, the volume intensity curves were drawn for the standard and audition music, respectively. Figure2 is a schematic diagram of a standard volume intensity curve (a) and a tester’s volume intensity curve (b).Figure 2
Schematic diagram of volume intensity curve.
(a)
Schematic diagram of the standard volume intensity curve(b)
Schematic diagram of the tester’s volume intensity curveIn the pitch comparison, the fundamental frequency traces of the two pieces of music are obtained by the cepstrum method, respectively, as shown in Figure3; (a) is the standard fundamental frequency trace, and (b) is the trial singing fundamental frequency trace [19]. At the same time, the average value of the standard fundamental frequency can be obtained: avgF0=143.12HZ, and the average value of the fundamental frequency of the trial singing is avgF0=142.05HZ.Figure 3
Schematic diagram of fundamental frequency trace.
(a)
Schematic diagram of standard fundamental frequency trace(b)
Schematic diagram of the fundamental frequency track of the trial singingTable1 shows the average distance and score of each parameter of the test singers. Among them, mindisv=726.126 of pitch, pathlength=144; mindisp=4.51987, pathlength=163 of pitch, breathsmoothness=484.20. It can be seen from Table 1 that the scoring system is more sensitive to pitch and breath stability, but not very sensitive to sound intensity. It can be seen that the fundamental frequency trace represents the highest importance, followed by the breath smoothness, and finally the volume intensity curve.Table 1
The average distance and score of each parameter of the auditioners.
Characteristic parametersSound intensityPitchBreath stabilityFinal scoreErrorMachine scoring0.0177.4687.6575.122.442%Human scoring5808577Etude library evaluation results. In the machine scoring, through experiments we set the weights of the three feature parameters to be 5%, 80%, and 15%, respectively.


## 4.1. Scoring Test Results

The scoring mechanism of the singing scoring system is based on the comparison of the above-mentioned breath, sound intensity, and pitch characteristics; the auditioner can see not only the scores in real time, but also the final total score of the audition. The system scoring formula is shown in the following:(5)score=k11001+a1mindisib1+k21001+a2mindispb2+k31001+a3stb3.Among them,a1,a2,a1,b1,b2,b3>0, k1+k2+k3=1, and k1, k2, k3 are the weights of each scoring parameter in the scoring mechanism, and mindisv and mindisp are the distances calculated by the sound intensity and pitch parameters, respectively. st represents the breath stability parameter; through repeated experiments of machine scoring and manual scoring, the weights reflect the best mapping relationship between the two, so that the computer can better simulate the expert scoring. The following is an example of a simulation experiment conducted with the male voice e etude:The measurement of breath is a process of self-comparison. In the experiment, the smoothness of breath is measured by calculating the standard deviation of the test sound waveform. In the sound intensity comparison, the volume intensity curves were drawn for the standard and audition music, respectively. Figure2 is a schematic diagram of a standard volume intensity curve (a) and a tester’s volume intensity curve (b).Figure 2
Schematic diagram of volume intensity curve.
(a)
Schematic diagram of the standard volume intensity curve(b)
Schematic diagram of the tester’s volume intensity curveIn the pitch comparison, the fundamental frequency traces of the two pieces of music are obtained by the cepstrum method, respectively, as shown in Figure3; (a) is the standard fundamental frequency trace, and (b) is the trial singing fundamental frequency trace [19]. At the same time, the average value of the standard fundamental frequency can be obtained: avgF0=143.12HZ, and the average value of the fundamental frequency of the trial singing is avgF0=142.05HZ.Figure 3
Schematic diagram of fundamental frequency trace.
(a)
Schematic diagram of standard fundamental frequency trace(b)
Schematic diagram of the fundamental frequency track of the trial singingTable1 shows the average distance and score of each parameter of the test singers. Among them, mindisv=726.126 of pitch, pathlength=144; mindisp=4.51987, pathlength=163 of pitch, breathsmoothness=484.20. It can be seen from Table 1 that the scoring system is more sensitive to pitch and breath stability, but not very sensitive to sound intensity. It can be seen that the fundamental frequency trace represents the highest importance, followed by the breath smoothness, and finally the volume intensity curve.Table 1
The average distance and score of each parameter of the auditioners.
Characteristic parametersSound intensityPitchBreath stabilityFinal scoreErrorMachine scoring0.0177.4687.6575.122.442%Human scoring5808577Etude library evaluation results. In the machine scoring, through experiments we set the weights of the three feature parameters to be 5%, 80%, and 15%, respectively.


## 5. Conclusion

The challenges of the information society to various fields of music education are obvious, the introduction of advanced science and technology and the use of advanced methods and means for vocal music teaching are an inevitable trend for the improvement and development of the discipline. The author proposes a vocal music teaching method based on VC++, applies multimedia technology to traditional vocal music teaching, and develops a set of simple interactive multimedia vocal music teaching system [20]. This method breaks through the limitations of traditional vocal music teaching and can provide students with an interactive teaching and self-study platform with skill guidance and correction functions. This will change the dull and single status quo of vocal music teaching and greatly improve the intuition, effectiveness, interactivity, and learnability of the vocal music teaching process. It is not only original, but also has high practical value.



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*Source: 6282581-2022-08-27.xml*