# Research Based on the Application and Exploration of Artificial Intelligence in the Field of Traditional Music

Authors: Yi Yang; Xia Huang
Journal: Journal of Sensors
 (2022)

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## Abstract

Traditional Chinese music has undergone trials and tribulations. To date, traditional music has been gradually improved, preserved, and passed down, both in terms of theoretical works and traditional music varieties. However, the current state of traditional music is still a cause for concern. Whether it is scholars engaged in the study of traditional music, universities, or local government agencies, there are still areas that need to be improved individually. The need to cultivate a future audience for traditional music and to make full use of new media such as the Internet is a priority at this stage. The emergence of any new technology is bound to have some impact on the existing social system, and the emergence of artificial intelligence is no exception. Due to the limitation of technology, only a small number of basic applications have been developed, and it is the future mission of research workers whether they can develop more advanced products for music lovers to experience on the basis of ensuring the basic maturity of these applications. In this paper, we introduce the convolutional deep belief network model based on the restricted Boltzmann machine and apply the convolutional deep belief network algorithm to the music melody recognition. Firstly, it was pretrained by an unsupervised greedy layer-by-layer algorithm. Then, the network parameters were fine-tuned by a supervised network training method, and the recognition ability of the model was improved by adjusting the network parameters. The experimental results show that the recognition effect of the system is more obvious under the condition that the length of music samples is 3 s, and the recognition effect is better when the number of model layers is 2 than that when the number of layers is 1.


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## Body

## 1. Introduction

Traditional Chinese music is music with national characteristics created by the Chinese people using methods unique to the Chinese people and in unique forms. The unique musical forms of the Chinese people can be divided into two kinds: one is the visible and tangible material form, and the other is the invisible and intangible spiritual form. The invisible and intangible things like Peking Opera and Kunqu, which can be enjoyed by people, are the inherent forms of the nation. The guzheng, pipa, erhu, etc., which are both visible and tangible, are the inherent forms of the nation.Since the Opium War, influenced by Western culture, Chinese traditional music has suffered a heavy blow during this period. China has a wide geographical area, many ethnic groups, and great differences between the north and the south, and the forms of traditional music are rich and diverse. In the face of the complex traditional music situation and the rapid development of economic construction, many varieties of traditional music have been left without successors and are on the verge of being lost. Scholars engaged in traditional Chinese music education and research have gradually established the new concept of “more preservation, less elimination,” which is a valuable concept exchanged with great cultural loss and historical lessons, and we should stick to it. At the same time, we should lead the scholars in universities to go out and dig, collect, examine, and organize the traditional folklore and traditional music cultural heritage in their locations, or let the traditional culture into the campus, so as to experience the traditional music more intuitively. Workshops and seminars of different scales have been held to give university scholars a more systematic and in-depth understanding of traditional music. The government has played an integral role in the preservation of traditional Chinese music. Local governments have established a dominant role in the preservation of traditional music, enacting laws, setting up institutions, increasing investment, improving programs, and establishing a system of transmitters, but this governmental involvement has also brought some disadvantages [1]. In some seemingly prosperous and thriving intangible cultural heritage projects, there is still a discontinuity. More often than not, they focus on theatrical performance, using modern advanced technology, adding choreography, sound, light, and electricity, so the purity and originality of folk music is lost, and only becomes an advertisement for the business and tourism industry to make profits. With the rapid development of the economy, technology has gradually penetrated into our lives and changed our way of life. Machines have replaced human labor, the labor trumpets have gradually disappeared, and the intervention of cell phones has gradually replaced the mountain songs by WeChat. Folk artists are also gradually replaced by electronic audio and new media, and more young people choose to go out of their hometowns and go to big cities to work hard, unwilling to learn a craft from their masters to support themselves. This has also put traditional music under greater impact, and the absence of inheritors means the disappearance of a traditional music. At the same time, the intervention of new media has given us a different aesthetic experience of music. The younger generation is more interested in the new wave of pop music and is no longer interested in traditional music.The perilous situation of Chinese traditional music has attracted the attention of all sectors of society, as has the preservation of endangered species. There are two ways of preserving traditional music: one is “museum-style” preservation, which aims to accurately preserve traditional music in its original form [2]. In this way, future generations can hear and see the sounds and colors of traditional music that have disappeared. The main purpose is to preserve traditional music that is on the verge of being lost and has special value. Our country is vast and rich in traditional music, and to this day there are still undiscovered varieties of traditional music that are disappearing at an accelerated rate. It means that we have not protected the traditional music enough, and we should mobilize the strength of all departments and institutions to protect the traditional music to the maximum within the limited time. The second is that it survives in modern musical life [3]. That is, it exists in the colorful folk music life as well as in the modern professional music scene. When it comes to the dynamic preservation of traditional music, one has to mention composers. The most important thing to let tradition live is to adapt it to the aesthetics of contemporary young people, to meet their pursuits, and to create new musical models. The traditional elements of music are blended with modern music, trying to “change the shape of the music.” This effectively preserves the essence of traditional music, while allowing more young people to accept and appreciate the beauty of traditional music.Tones are the smallest units of music, and the combination of tones in a connected way, under certain logical changes, produces music [4]. Fundamentally, the musical tones in music originate from the metrical system, and different methods of calculating the metrical system result in different pitches of musical tones, which are combined together to form different musical systems. The difference of musical system means the difference of scale arrangement, which indirectly affects the presentation of tuning and the expression of musical functions. In its long history of development, Chinese traditional music has produced a variety of metrical calculation methods, forming a large system of metrical systems, including the three-part loss and gain meter, the twelve mean meter, and the pure meter. Different metrical calculation methods represent different metrical systems. The calculated musical pitch and scale systems also have obvious differences. According to the needs of musical expression and style, different systems of meter are used in different types of music and instruments. For example, the traditional music of the north is based on the three-point loss and gain rhythm, the southern music such as Cantonese music is based on the twelve mean rhythm, and the Guqin uses both the twelve mean rhythm and the pure rhythm system. Because of the differences in the scales of the different meters, it is impossible to achieve complete harmony across the musical genres, which fundamentally determines that traditional Chinese music cannot build a three-dimensional relationship between tones like Western music, but can only emphasize the sense of connection and extension between tones horizontally. In terms of musical expression, the focus is on the expression of each tone, i.e., the “tone cavity” as the focus of musical development.With the popularity of the Internet, a technical invention linked to it is quietly entering the life of the public [5]. In recent years, the rise of big data, cloud computing, artificial intelligence, and other technologies has accelerated the development of various industries, and “Internet+” “big data+” “AI+” has gradually become the new normal of social development [6, 7]. In this context, the combination of technology and music has been a major trend, and the development of traditional music has also ushered in a new opportunity in this change. The integration and development of technologies such as big data and artificial intelligence with traditional music has greatly innovated the form of music dissemination and popularization, transforming the path of music dissemination from the original, one-way transmission by word of mouth to a multidimensional, diversified dissemination that is not restricted by geography or time and space. The application of artificial intelligence in the field of traditional music has brought a new artistic experience to the public. With the continuous innovation and breakthrough of information technology, the applications in the field of traditional music are gradually enriched, among which the more prominent ones are in the melody recognition, intelligent composition, instrument simulation, and virtual performance.The main contributions of this paper are as follows: (1) Firstly, it is an important task to analyze the accurate identification of traditional music melodies and retrieve the corresponding music information based on the melodies. This is crucial for the dissemination and preservation of traditional music. (2) Combined with artificial intelligence techniques, this paper investigates a traditional music melody recognition method based on deep neural networks. (3) By extracting the depth features in the frequency domain, the melody features are accurately identified and combined with a neural network classifier; the corresponding tracks are retrieved.


## 2. Related Works

### 2.1. Current Status of Traditional Music Research

Traditional music culture is a very important part of Chinese traditional culture. Therefore, the inheritance and development of Chinese traditional music culture has a very positive significance for the promotion and development of traditional culture, and it is a very effective way to promote the development of culture by music development [8]. In most people’s opinion, learning music is more interesting than learning words. Therefore, it is possible for the public to inherit the national culture by learning traditional music, which not only helps to diversify the national culture but also allows the public to feel the national spirit in traditional music. Each nation has its own unique cultural heritage. Therefore, the public can feel the excellent culture of the nation through music, and in the process of learning music, they can continuously inherit the national culture, feel the essence of its spirit, and promote the development of the whole nation. With the continuous development of China’s social economy, people’s material life is constantly satisfied, but only the material enjoyment is far from enough; the spiritual life also needs to keep pace with the material progress. Most of the modern music works are marketed works, and the profit nature is obvious, which is not conducive to the cultivation of people’s sentiments. But Chinese traditional music is different. Traditional Chinese music contains the kind and simple spirit of the Chinese working people, who have gained insights and created songs in their labor, or composed songs that represent their surging emotions because of their love for life. If this traditional music could be integrated into the lives of modern people, their spiritual and cultural dimensions would be enhanced.All countries and ethnic groups in the world have their own traditional culture, and traditional culture is a kind of culture reflecting the characteristics and style of the nation, which is brought together by the evolution of civilization; therefore, the traditional culture of each nation is different. Chinese traditional music is a very attractive music culture that has developed under such fertile cultural soil. Chinese traditional music culture not only reflects the history of Chinese culture but also to a certain extent reflects the changes of modern culture. With the rapid development of social economy and increasingly rapid globalization, China is in very close contact with the outside world. Various cultures from the West have entered China through different ways and have had a great impact on Chinese cultural thought, and Western musical culture has also had a great impact on traditional Chinese musical culture. The way traditional Chinese music culture is transmitted and passed on has changed dramatically due to the intervention of Western culture. However, most of the Chinese traditional music culture has disappeared because of its inability to adapt to the new performance methods, which has put the Chinese traditional music culture into a crisis. Chinese traditional music culture is part of the world culture; therefore, the influence of the world culture on Chinese culture is actually very important, and we should treat Chinese traditional music culture with reverence and protection. How to protect and inherit the excellent Chinese traditional music culture, let the environment of Chinese traditional culture be well managed, and constantly seek the path of innovation in the case of foreign cultural intervention, and realize the sustainable development of Chinese traditional music culture, not only has great benefits for everyone and for the society but also helps a lot to improve the influence of Chinese nation in the world culture [9].Melody is generally expressed in music in two basic forms, filling the main vocal parts of polyphonic music or existing independently as monophonic music; the former is represented by Western music, while the latter conveys the main characteristics of traditional Chinese music [10]. Limited by the three-dimensional thinking characteristic of Western culture, the development of Western music is more focused on wholeness and vertical logical construction, with melodic changes involving not only the horizontal ebb and flow of the tune line, but also the coordination of vertical harmonic and polyphonic relationships. It can be said that melody is only a part of the many vocal parts of Western music, and the development of tunes includes but is not limited to the change of the main melodic vocal part, which involves the joint cooperation between several vocal parts. Most traditional Chinese music is a single-line music, and the melodic voice part has an irreplaceable place in the piece. Some music even contains only a single melodic voice part, such as erhu, flute, suona, xiao, and other folk instruments with monophonic attributes, whose solo repertoire plays only a single melodic voice part. In the case of guzheng, pipa, and other multivoice instruments, as well as opera, folk songs, and folk music ensembles, although they can play two or even more voices at the same time, the dominant role is still played by the main melodic voice, and the development of other voices is based on the main melodic voice as the core of the minor variation. In essence, this ensemble can be characterized as a collection of multiple instruments interpreting the main theme in their different ways. Thus, the Chinese style of polyphonic tune development shows not the vertical coordination of musical tones but rather the emphasis on the horizontal echoing and modification of the main melody by each voice part.


### 2.2. Current Research on Artificial Intelligence in Music

Since the emergence of new music culture, the development of traditional music has gradually fallen into a dilemma. It has been the people’s urgent pursuit to revitalize and promote the national culture, but geographical, institutional, and financial constraints have led to a modernization crisis in the development of traditional music as well. Along with the popularity of the Internet, the inheritance and development of traditional music seems to have found a new opportunity. The combination of information technology and music can be traced back to the creation of “electronic music” in the early 20th century, an innovation that revolutionized the traditional status quo of music being dependent on the human voice and musical instruments. Then, driven by technology, music went through a digital era, mainly in the form of digital scores and digital sequencing, represented by MIDI [11]. Nowadays, the fusion of AI and traditional music has brought us a new experience of music art, and the term “Music AI” (Music Artificial Intelligence) has emerged [12]. Take the way the guqin is played in traditional music as an example. In the early days, the ancient people invented the diminutive score in order to preserve the fingering, string order, and pitch of the guqin, but, unfortunately, most of the scores have been lost in the flood of history.The Shanghai Conservatory of Music and the Huawei team are working on how to use AI to decipher these “heavenly books” and reconstruct ancient music. The process requires a computer programmer to program the notation rules of the reduced-word scores, and then a smart device to take pictures of the scores and translate them into short scores for inclusion in the database. Then, through cloud computing and other technical means, the neural network model is trained on the server to carry out deep learning of the diminutive scores, so that the AI has the ability to recognize the pitch pattern of the diminutive scores, and this operation is to return the traditional music to its original nature with the help of AI in modern society [13, 14]. Since ancient times, there are countless melodic fragments with various characteristics, and it is difficult to find a piece of music from the preserved melodies, but human beings have used their wisdom to find a solution. So far, the function of “melody recognition” has gone through three stages: first, the human self-identification stage, which requires the recognizer to have a large reserve of musical melodies, which is obviously more difficult; second, the audio file input recognition, which must be done by computer, and the operator needs to have skilled computer operation skills. The third is the intelligent recognition with the help of AI technology, which is based on the voice recognition function of intelligent devices and not only broadens the traditional music retrieval method but also shortens the retrieval time. Similar to the traditional way of retrieving a given audio file, intelligent recognition requires the device to first store a large amount of pitch, melody, rhythm, and other basic components of music. The recording of human and instrumental sounds is required to form a music data resource library inside the device, and then computer algorithms are used to classify and form a melody feature library and compile feature codes for each timbre and each melody. Compared with the search method for a given audio file, AI intelligent recognition does not require tedious steps such as format conversion and file input, and can directly recognize the melody according to the music played. When the intelligent device receives a melody signal, it will use computer algorithms to filter and identify whether the melody fragment exists in the existing music data library of the device, and then extract the pitch and rhythm signals that match the library and match them with the existing melody to arrive at the answer [15]. This function not only identifies the name of the music fragment by the melody fragment but also presents the relevant information of the music, such as composer, performer, and composition background, in a comprehensive manner by using the Internet and big data [16].A common feature of early melodic feature representations is the autocorrelation method of pitch extraction in the time domain. The melody is represented as U, D, S or U, D, R, indicating that the pitch of a note is higher, lower, or equal to the previous note of this note, respectively. Based on this representation, the melodic feature matching algorithm is transformed into a string matching algorithm. This representation basically does not consider the rhythmic characteristics of the music, and these methods achieved a hit rate of 70%-90% in nearly 10,000 songs. The drawback of this approach is that the music files are required to be in MIDI format, since MIDI describes melodies in sequences of events that are easily converted into the sequence of notes required by the feature matching algorithm. Deep learning is an emerging branch in the field of machine learning that is able to extract more abstract features of data by simulating the structural features of the human brain [17, 18]. Applying convolutional neural networks in melody extraction requires first converting sound files into image files that are convenient for convolutional neural networks to process, and then performing feature extraction with the help of some methods of image feature extraction, and these converted images can be sound spectrograms, and this method that requires converting sounds into images before performing melody extraction is a challenge in terms of computational effort [19].


## 2.1. Current Status of Traditional Music Research

Traditional music culture is a very important part of Chinese traditional culture. Therefore, the inheritance and development of Chinese traditional music culture has a very positive significance for the promotion and development of traditional culture, and it is a very effective way to promote the development of culture by music development [8]. In most people’s opinion, learning music is more interesting than learning words. Therefore, it is possible for the public to inherit the national culture by learning traditional music, which not only helps to diversify the national culture but also allows the public to feel the national spirit in traditional music. Each nation has its own unique cultural heritage. Therefore, the public can feel the excellent culture of the nation through music, and in the process of learning music, they can continuously inherit the national culture, feel the essence of its spirit, and promote the development of the whole nation. With the continuous development of China’s social economy, people’s material life is constantly satisfied, but only the material enjoyment is far from enough; the spiritual life also needs to keep pace with the material progress. Most of the modern music works are marketed works, and the profit nature is obvious, which is not conducive to the cultivation of people’s sentiments. But Chinese traditional music is different. Traditional Chinese music contains the kind and simple spirit of the Chinese working people, who have gained insights and created songs in their labor, or composed songs that represent their surging emotions because of their love for life. If this traditional music could be integrated into the lives of modern people, their spiritual and cultural dimensions would be enhanced.All countries and ethnic groups in the world have their own traditional culture, and traditional culture is a kind of culture reflecting the characteristics and style of the nation, which is brought together by the evolution of civilization; therefore, the traditional culture of each nation is different. Chinese traditional music is a very attractive music culture that has developed under such fertile cultural soil. Chinese traditional music culture not only reflects the history of Chinese culture but also to a certain extent reflects the changes of modern culture. With the rapid development of social economy and increasingly rapid globalization, China is in very close contact with the outside world. Various cultures from the West have entered China through different ways and have had a great impact on Chinese cultural thought, and Western musical culture has also had a great impact on traditional Chinese musical culture. The way traditional Chinese music culture is transmitted and passed on has changed dramatically due to the intervention of Western culture. However, most of the Chinese traditional music culture has disappeared because of its inability to adapt to the new performance methods, which has put the Chinese traditional music culture into a crisis. Chinese traditional music culture is part of the world culture; therefore, the influence of the world culture on Chinese culture is actually very important, and we should treat Chinese traditional music culture with reverence and protection. How to protect and inherit the excellent Chinese traditional music culture, let the environment of Chinese traditional culture be well managed, and constantly seek the path of innovation in the case of foreign cultural intervention, and realize the sustainable development of Chinese traditional music culture, not only has great benefits for everyone and for the society but also helps a lot to improve the influence of Chinese nation in the world culture [9].Melody is generally expressed in music in two basic forms, filling the main vocal parts of polyphonic music or existing independently as monophonic music; the former is represented by Western music, while the latter conveys the main characteristics of traditional Chinese music [10]. Limited by the three-dimensional thinking characteristic of Western culture, the development of Western music is more focused on wholeness and vertical logical construction, with melodic changes involving not only the horizontal ebb and flow of the tune line, but also the coordination of vertical harmonic and polyphonic relationships. It can be said that melody is only a part of the many vocal parts of Western music, and the development of tunes includes but is not limited to the change of the main melodic vocal part, which involves the joint cooperation between several vocal parts. Most traditional Chinese music is a single-line music, and the melodic voice part has an irreplaceable place in the piece. Some music even contains only a single melodic voice part, such as erhu, flute, suona, xiao, and other folk instruments with monophonic attributes, whose solo repertoire plays only a single melodic voice part. In the case of guzheng, pipa, and other multivoice instruments, as well as opera, folk songs, and folk music ensembles, although they can play two or even more voices at the same time, the dominant role is still played by the main melodic voice, and the development of other voices is based on the main melodic voice as the core of the minor variation. In essence, this ensemble can be characterized as a collection of multiple instruments interpreting the main theme in their different ways. Thus, the Chinese style of polyphonic tune development shows not the vertical coordination of musical tones but rather the emphasis on the horizontal echoing and modification of the main melody by each voice part.


## 2.2. Current Research on Artificial Intelligence in Music

Since the emergence of new music culture, the development of traditional music has gradually fallen into a dilemma. It has been the people’s urgent pursuit to revitalize and promote the national culture, but geographical, institutional, and financial constraints have led to a modernization crisis in the development of traditional music as well. Along with the popularity of the Internet, the inheritance and development of traditional music seems to have found a new opportunity. The combination of information technology and music can be traced back to the creation of “electronic music” in the early 20th century, an innovation that revolutionized the traditional status quo of music being dependent on the human voice and musical instruments. Then, driven by technology, music went through a digital era, mainly in the form of digital scores and digital sequencing, represented by MIDI [11]. Nowadays, the fusion of AI and traditional music has brought us a new experience of music art, and the term “Music AI” (Music Artificial Intelligence) has emerged [12]. Take the way the guqin is played in traditional music as an example. In the early days, the ancient people invented the diminutive score in order to preserve the fingering, string order, and pitch of the guqin, but, unfortunately, most of the scores have been lost in the flood of history.The Shanghai Conservatory of Music and the Huawei team are working on how to use AI to decipher these “heavenly books” and reconstruct ancient music. The process requires a computer programmer to program the notation rules of the reduced-word scores, and then a smart device to take pictures of the scores and translate them into short scores for inclusion in the database. Then, through cloud computing and other technical means, the neural network model is trained on the server to carry out deep learning of the diminutive scores, so that the AI has the ability to recognize the pitch pattern of the diminutive scores, and this operation is to return the traditional music to its original nature with the help of AI in modern society [13, 14]. Since ancient times, there are countless melodic fragments with various characteristics, and it is difficult to find a piece of music from the preserved melodies, but human beings have used their wisdom to find a solution. So far, the function of “melody recognition” has gone through three stages: first, the human self-identification stage, which requires the recognizer to have a large reserve of musical melodies, which is obviously more difficult; second, the audio file input recognition, which must be done by computer, and the operator needs to have skilled computer operation skills. The third is the intelligent recognition with the help of AI technology, which is based on the voice recognition function of intelligent devices and not only broadens the traditional music retrieval method but also shortens the retrieval time. Similar to the traditional way of retrieving a given audio file, intelligent recognition requires the device to first store a large amount of pitch, melody, rhythm, and other basic components of music. The recording of human and instrumental sounds is required to form a music data resource library inside the device, and then computer algorithms are used to classify and form a melody feature library and compile feature codes for each timbre and each melody. Compared with the search method for a given audio file, AI intelligent recognition does not require tedious steps such as format conversion and file input, and can directly recognize the melody according to the music played. When the intelligent device receives a melody signal, it will use computer algorithms to filter and identify whether the melody fragment exists in the existing music data library of the device, and then extract the pitch and rhythm signals that match the library and match them with the existing melody to arrive at the answer [15]. This function not only identifies the name of the music fragment by the melody fragment but also presents the relevant information of the music, such as composer, performer, and composition background, in a comprehensive manner by using the Internet and big data [16].A common feature of early melodic feature representations is the autocorrelation method of pitch extraction in the time domain. The melody is represented as U, D, S or U, D, R, indicating that the pitch of a note is higher, lower, or equal to the previous note of this note, respectively. Based on this representation, the melodic feature matching algorithm is transformed into a string matching algorithm. This representation basically does not consider the rhythmic characteristics of the music, and these methods achieved a hit rate of 70%-90% in nearly 10,000 songs. The drawback of this approach is that the music files are required to be in MIDI format, since MIDI describes melodies in sequences of events that are easily converted into the sequence of notes required by the feature matching algorithm. Deep learning is an emerging branch in the field of machine learning that is able to extract more abstract features of data by simulating the structural features of the human brain [17, 18]. Applying convolutional neural networks in melody extraction requires first converting sound files into image files that are convenient for convolutional neural networks to process, and then performing feature extraction with the help of some methods of image feature extraction, and these converted images can be sound spectrograms, and this method that requires converting sounds into images before performing melody extraction is a challenge in terms of computational effort [19].


## 3. Algorithm Design

### 3.1. Convolutionally Restricted Boltzmann Machine

In the standard restricted Boltzmann machine (RBM) [20], all observed variables are related to different parameters in the hidden layer. For the convenience of explanation and understanding, the model principle is illustrated below from the image perspective. When using the RBM to extract global features from a complete image, no consideration is given to how large the image is. This is because when the dimension of the image increases, the number of connected weights in the RBM becomes very large, resulting in complicating and slowing down the operations during training updates and other processes. In fact, only a small number of parameters are needed when tracing spatially local features of an image, and these parameters provide reusability when extracting features from other different places of the image. Therefore, to solve this problem, an extension of the RBM model, the convolutionally restricted Boltzmann machine (CRBM), is proposed.CRBM is very similar to RBM in that it is a model consisting of a visible layer and a hidden layer, both of which are matrices of random variables. The visible layer matrix in CRBM can be represented by a pair of images, and local blocks of images can represent parts of the visible layer matrix (sub-windows). Local perceptual field and weight sharing are features of CRBM; in other words, the hidden and visible layers are locally connected to each other and the weights of the model are shared. A CRBM feature extractor is shown in Figure1. A feature map represents each cluster of hidden layer units. Each feature map is a binary matrix that represents a single feature in different positions of the input variables, i.e., each feature map represents one feature. Thus, the hidden layer cells can be separated into different feature maps so as to represent the different features of the visible layer cells at different locations.Figure 1
CRBM feature extractor.From Figure1, we can see that CRBM is connected to the visible layer cell V and the hidden layer cell H by K3×3 feature extractors: W1W2,⋯Wk. The hidden layer cells are separated into K submatrices called feature maps: swab, from H1,H2,⋯Hk. Each hidden layer cell is represented as a specific feature extracted from a visible layer 3×3 neighborhood cell. A cell in a feature map represents the same feature in different places in a visible cell V.


### 3.2. Convolutional Deep Belief Network

Convolutional deep belief networks (CDBN) is a hierarchical probabilistic generative model based on a stack of CRBMs. The difference between deep belief networks (DBNs) [21] and CDBNs lies in the use of convolution, which can also be interpreted as a neural network composed of DBNs combined with the features of CNNs (i.e., local perceptual field and weight sharing in the model). After obtaining the features of the training samples by convolution, if we use these obtained features directly, we may face a very large amount of computation [22, 23]. And learning a classifier with a very high dimensional feature input is inconvenient and prone to overfitting. To solve such a problem, the convolved features are likely to be applicable in different regions of the sample, and to describe samples with large dimensionality, one solution is to perform aggregation statistics on features at different locations [24]. One can calculate the average (or maximum) value of a particular feature over a region of the sample. This reduces the dimensionality and at the same time is less prone to overfitting. This aggregation operation is called pooling. In another way, pooling can also be understood as downsampling.The CRBM is refined using probabilistic maximum pooling [25], which is the operation of pooling the maximum value of a neighborhood of the hidden layer in a probabilistic manner, and the CRBM with probabilistic maximum pooling is used as the basic component of CDBN in this paper, and the model is shown in Figure 2.Figure 2
CDBN model structure.The spectral features of the training and testing data are first extracted from the original music data. The spectral features of the music data are used as the input of the CDBN network to extract the CDBN features of its first and second layers, respectively. The first and second layers of CDBN are pretrained by an unsupervised greedy layer-by-layer algorithm. The music features extracted from the CDBN correspond to CDBN1 and CDBN2.The first layer of CDBN has a baseK of 300 groups, a filter length Wk of 6, and a maximum pooling size of 3. The second layer K has 300 groups, a Wk of 6, and a maximum pooling size of 30. Firstly, the spectral features are extracted for each segment of the music data of the training data, and the music signal is represented in the form of a spectral vector. The Fourier transform of each signal frame is obtained to represent the music data, and the length of each speech signal frame is 20 ms, and the overlapping region is 10 ms, as shown in Figure 3.Figure 3
The process of obtaining a frequency domain signal from a time domain signal.CDBN is pretrained by an unsupervised greedy layer-by-layer algorithm. After all layers are pretrained, the output layer with nodeL is added to the top of the network, and then the network parameters are fine-tuned by a supervised network training method, which in turn leads to better network performance. The optimization goal of the neural network is to optimize Ein by adjusting the parameters W so that the final model output is closest to the target value, and it is necessary to find ∂en/∂wijl.
(1)en=yn−s1L2=yn−∑i=0dL−1wi1Lx1L−12.For the output layer:0≤i≤dL−1(2)∂en∂wi1L=∂en∂s1L∂s1L∂wi1L=−2yn−s1LxiL−1.For other layers:1≤1≤L;0≤i≤di−1;1≤j≤dl(3)∂en∂wt1l=∂en∂sjl∂s1l∂wi1l=−2δjlxii−1.From the above, it is clear thatδ1L=−2yn−s1L. The other δjl is solved as follows:
(4)δj′=∂en∂sjl,δjl⇒xjl⇒xils1l+1⋮skl+1⋮⇒⋯⇒en.Here,δjl can be calculated from δjl+1, δjL is known, so δjl can be obtained by reverse calculation. Thus, the final network parameters can be obtained from the input forward to the output, and then from the error backward to solve the parameter differentiation for parameter optimization. The parameters Wk,b,c of the CDBN are obtained by learning from the contrast scatter of the input training sample set. The values of the hidden variables are obtained by reconstructing the distribution of the CDBN from the input units using Gibbs sampling, and then the values of the observed variables are obtained from the hidden units.


## 3.1. Convolutionally Restricted Boltzmann Machine

In the standard restricted Boltzmann machine (RBM) [20], all observed variables are related to different parameters in the hidden layer. For the convenience of explanation and understanding, the model principle is illustrated below from the image perspective. When using the RBM to extract global features from a complete image, no consideration is given to how large the image is. This is because when the dimension of the image increases, the number of connected weights in the RBM becomes very large, resulting in complicating and slowing down the operations during training updates and other processes. In fact, only a small number of parameters are needed when tracing spatially local features of an image, and these parameters provide reusability when extracting features from other different places of the image. Therefore, to solve this problem, an extension of the RBM model, the convolutionally restricted Boltzmann machine (CRBM), is proposed.CRBM is very similar to RBM in that it is a model consisting of a visible layer and a hidden layer, both of which are matrices of random variables. The visible layer matrix in CRBM can be represented by a pair of images, and local blocks of images can represent parts of the visible layer matrix (sub-windows). Local perceptual field and weight sharing are features of CRBM; in other words, the hidden and visible layers are locally connected to each other and the weights of the model are shared. A CRBM feature extractor is shown in Figure1. A feature map represents each cluster of hidden layer units. Each feature map is a binary matrix that represents a single feature in different positions of the input variables, i.e., each feature map represents one feature. Thus, the hidden layer cells can be separated into different feature maps so as to represent the different features of the visible layer cells at different locations.Figure 1
CRBM feature extractor.From Figure1, we can see that CRBM is connected to the visible layer cell V and the hidden layer cell H by K3×3 feature extractors: W1W2,⋯Wk. The hidden layer cells are separated into K submatrices called feature maps: swab, from H1,H2,⋯Hk. Each hidden layer cell is represented as a specific feature extracted from a visible layer 3×3 neighborhood cell. A cell in a feature map represents the same feature in different places in a visible cell V.


## 3.2. Convolutional Deep Belief Network

Convolutional deep belief networks (CDBN) is a hierarchical probabilistic generative model based on a stack of CRBMs. The difference between deep belief networks (DBNs) [21] and CDBNs lies in the use of convolution, which can also be interpreted as a neural network composed of DBNs combined with the features of CNNs (i.e., local perceptual field and weight sharing in the model). After obtaining the features of the training samples by convolution, if we use these obtained features directly, we may face a very large amount of computation [22, 23]. And learning a classifier with a very high dimensional feature input is inconvenient and prone to overfitting. To solve such a problem, the convolved features are likely to be applicable in different regions of the sample, and to describe samples with large dimensionality, one solution is to perform aggregation statistics on features at different locations [24]. One can calculate the average (or maximum) value of a particular feature over a region of the sample. This reduces the dimensionality and at the same time is less prone to overfitting. This aggregation operation is called pooling. In another way, pooling can also be understood as downsampling.The CRBM is refined using probabilistic maximum pooling [25], which is the operation of pooling the maximum value of a neighborhood of the hidden layer in a probabilistic manner, and the CRBM with probabilistic maximum pooling is used as the basic component of CDBN in this paper, and the model is shown in Figure 2.Figure 2
CDBN model structure.The spectral features of the training and testing data are first extracted from the original music data. The spectral features of the music data are used as the input of the CDBN network to extract the CDBN features of its first and second layers, respectively. The first and second layers of CDBN are pretrained by an unsupervised greedy layer-by-layer algorithm. The music features extracted from the CDBN correspond to CDBN1 and CDBN2.The first layer of CDBN has a baseK of 300 groups, a filter length Wk of 6, and a maximum pooling size of 3. The second layer K has 300 groups, a Wk of 6, and a maximum pooling size of 30. Firstly, the spectral features are extracted for each segment of the music data of the training data, and the music signal is represented in the form of a spectral vector. The Fourier transform of each signal frame is obtained to represent the music data, and the length of each speech signal frame is 20 ms, and the overlapping region is 10 ms, as shown in Figure 3.Figure 3
The process of obtaining a frequency domain signal from a time domain signal.CDBN is pretrained by an unsupervised greedy layer-by-layer algorithm. After all layers are pretrained, the output layer with nodeL is added to the top of the network, and then the network parameters are fine-tuned by a supervised network training method, which in turn leads to better network performance. The optimization goal of the neural network is to optimize Ein by adjusting the parameters W so that the final model output is closest to the target value, and it is necessary to find ∂en/∂wijl.
(1)en=yn−s1L2=yn−∑i=0dL−1wi1Lx1L−12.For the output layer:0≤i≤dL−1(2)∂en∂wi1L=∂en∂s1L∂s1L∂wi1L=−2yn−s1LxiL−1.For other layers:1≤1≤L;0≤i≤di−1;1≤j≤dl(3)∂en∂wt1l=∂en∂sjl∂s1l∂wi1l=−2δjlxii−1.From the above, it is clear thatδ1L=−2yn−s1L. The other δjl is solved as follows:
(4)δj′=∂en∂sjl,δjl⇒xjl⇒xils1l+1⋮skl+1⋮⇒⋯⇒en.Here,δjl can be calculated from δjl+1, δjL is known, so δjl can be obtained by reverse calculation. Thus, the final network parameters can be obtained from the input forward to the output, and then from the error backward to solve the parameter differentiation for parameter optimization. The parameters Wk,b,c of the CDBN are obtained by learning from the contrast scatter of the input training sample set. The values of the hidden variables are obtained by reconstructing the distribution of the CDBN from the input units using Gibbs sampling, and then the values of the observed variables are obtained from the hidden units.


## 4. Experiments

### 4.1. Experiment Preparation

The experimental data are traditional music collected on the web, with a total of 4394 music melodies, containing four categories: folk music, opera music, religious music, and ethnic instrumental music. 60% of them are used as the training set, 15% as the validation set, and 25% as the test set. Figure4 shows the specific dataset division.Figure 4
Dataset division.The experiments are built in the deep learning framework PyTorch environment, the experimental system environment Ubuntu 16.04 operating system, equipped with two Nivdia 1080TI graphics cards, 32G RAM. The network model is trained using Adam optimizer with 100 Epochs. The initial learning rate is 0.01 and the termination learning rate is 0.0001 with a cosine learning rate. The loss curves of the training and validation sets of the CBDN model proposed in this paper during the training process are shown in Figure5. The model converges after approximately 40 iterations. Figure 6 shows the accuracy of matching the output of the model with the target melody during the training process.Figure 5
Loss variation during training.Figure 6
Melody matching accuracy curve during training.


### 4.2. Ablation Experiments

In this paper, the first CDBN layer features and the second CDBN layer features are compared and the experimental results are shown in Table1. From the results in the table, we can see that the features learned in the first CDBN layer do not give better results than the features learned in the second CDBN layer for song fragment recognition.Table 1
Relationship between variation in the number of layers and recognition rate.
LayerTest set recognition rate (%)183.1289.2For different fragments as independent samples, different experiments were conducted in this paper, and the experimental results are shown in Table2. As can be seen from the table results, the short-time recognition performance is more superior, i.e., the recognition rate is higher when 3 s fragment statistical features are used as classification features.Table 2
Comparison of experiments with different lengths of music.
Duration of musicRecognition rate (%)10s68.568 s79.236 s82.595 s85.363 s87.71


### 4.3. Comparison Experiments with Other Models

We compared the model in this paper with several currently used music retrieval models on a database for web retrieval. The methods compared include principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN). These models can be broadly classified into two categories: linear and nonlinear, with the first two being linear methods and the neural network-based methods being nonlinear. We use the accuracy and recall curves to evaluate the models, and the results are shown in Figure7. It can be found that the traditional linear model does not perform well enough in retrieval, while the nonlinear model based on neural network has better performance in cross-modal tasks, and the CDBN in this paper works better than the traditional method. In addition, we also verified the model’s recognition effect on different categories of traditional music on the test set, and the results are shown in Table 3 and Figure 8. The music melodies of different categories are very different. It can be seen that the recognition rate of the method in this paper is more balanced for different categories, while the other methods tend to receive the influence of melodic distinction. This is due to the robust feature representation of the convolutional layer.Figure 7
Comparison of precision-recall curves of different models.Table 3
Recognition accuracy of different categories (%).
ModelFolkOperaReligiousInstrumentalPCA65.3972.7680.8776.33SVM69.3470.5882.9780.24ANN75.9676.8781.2983.22CDBN84.3286.7887.5685.94Figure 8
Recognition accuracy of different categories (%).


## 4.1. Experiment Preparation

The experimental data are traditional music collected on the web, with a total of 4394 music melodies, containing four categories: folk music, opera music, religious music, and ethnic instrumental music. 60% of them are used as the training set, 15% as the validation set, and 25% as the test set. Figure4 shows the specific dataset division.Figure 4
Dataset division.The experiments are built in the deep learning framework PyTorch environment, the experimental system environment Ubuntu 16.04 operating system, equipped with two Nivdia 1080TI graphics cards, 32G RAM. The network model is trained using Adam optimizer with 100 Epochs. The initial learning rate is 0.01 and the termination learning rate is 0.0001 with a cosine learning rate. The loss curves of the training and validation sets of the CBDN model proposed in this paper during the training process are shown in Figure5. The model converges after approximately 40 iterations. Figure 6 shows the accuracy of matching the output of the model with the target melody during the training process.Figure 5
Loss variation during training.Figure 6
Melody matching accuracy curve during training.


## 4.2. Ablation Experiments

In this paper, the first CDBN layer features and the second CDBN layer features are compared and the experimental results are shown in Table1. From the results in the table, we can see that the features learned in the first CDBN layer do not give better results than the features learned in the second CDBN layer for song fragment recognition.Table 1
Relationship between variation in the number of layers and recognition rate.
LayerTest set recognition rate (%)183.1289.2For different fragments as independent samples, different experiments were conducted in this paper, and the experimental results are shown in Table2. As can be seen from the table results, the short-time recognition performance is more superior, i.e., the recognition rate is higher when 3 s fragment statistical features are used as classification features.Table 2
Comparison of experiments with different lengths of music.
Duration of musicRecognition rate (%)10s68.568 s79.236 s82.595 s85.363 s87.71


## 4.3. Comparison Experiments with Other Models

We compared the model in this paper with several currently used music retrieval models on a database for web retrieval. The methods compared include principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN). These models can be broadly classified into two categories: linear and nonlinear, with the first two being linear methods and the neural network-based methods being nonlinear. We use the accuracy and recall curves to evaluate the models, and the results are shown in Figure7. It can be found that the traditional linear model does not perform well enough in retrieval, while the nonlinear model based on neural network has better performance in cross-modal tasks, and the CDBN in this paper works better than the traditional method. In addition, we also verified the model’s recognition effect on different categories of traditional music on the test set, and the results are shown in Table 3 and Figure 8. The music melodies of different categories are very different. It can be seen that the recognition rate of the method in this paper is more balanced for different categories, while the other methods tend to receive the influence of melodic distinction. This is due to the robust feature representation of the convolutional layer.Figure 7
Comparison of precision-recall curves of different models.Table 3
Recognition accuracy of different categories (%).
ModelFolkOperaReligiousInstrumentalPCA65.3972.7680.8776.33SVM69.3470.5882.9780.24ANN75.9676.8781.2983.22CDBN84.3286.7887.5685.94Figure 8
Recognition accuracy of different categories (%).


## 5. Conclusions

The combination of technology and art has become an unstoppable trend, and “AI+traditional music” has gradually penetrated into many aspects of singing, playing, and composing. Although there are still many problems to be solved, musicians still see new opportunities in the innovation path of Chinese traditional music, and we are looking forward to more applications being developed and put into use. Let us take a positive attitude and, with the attention of the national government and the care of the community, rely on the power of technology and academia to build a new ecological civilization, so that traditional Chinese music can have a broader future with the help of AI. Throughout thousands of years of history and civilization, society has always been in a constant state of change and progress, and the same is true for the development of traditional music. AI, as a product of the technological revolution of the new era, provides new ideas and methods for the dissemination of traditional music. In order to achieve sustainable development, traditional music must be in line with the times. In the current development situation, it seems that the combination of AI and traditional music will have a new and broader development prospect, but we must also work on it from various aspects.To address the shortcomings of existing algorithms in traditional music melody recognition and retrieval, this paper introduces deep learning theory and applies it to the process of music information retrieval. This paper introduces the CRBM model and convolutional deep belief network and applies the deep belief network algorithm to song recognition. The effects of traditional music sample length and the number of model layers on the recognition performance are investigated experimentally. The experimental results show that the method in this paper can accurately identify and retrieve music based on melody. In the future, we plan to conduct research on the use of recurrent neural networks for applications and exploration in the field of traditional music.



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*Source: 5093044-2022-07-05.xml*