# The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet

Authors: Xin Wang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Under the trend of rapid development and popularization of modern computer technology and information technology, the field of music education has also penetrated and integrated advanced computer technology and information technology. As an important branch of computer science, artificial intelligence involves many cross-cutting comprehensive subject content. As an indispensable part of our life, music is an art that reflects the emotions of human real life, and it is an ideology older than language. The rapid development of technology has injected many new elements into music, gradually changing the way people write, perform, produce, and enjoy music. The Internet has injected new elements and content into music education. It has transformed the original methods of writing, performing, producing, and appreciating music education and has had a great impact on music education concepts and teaching models. The future development trend of music education makes music education reach a new height under the application of artificial intelligence technology. Based on the current research results at home and abroad, this thesis closely integrates music theory and computational methods and advocates the comprehensive application of music theory, cognitive psychology, music cognition, neuroscience, artificial intelligence, signal processing, and other theories to solve music problems. Signal analysis identifies problems. In-depth discussion of the possibility and development trend of artificial intelligence technology applied to music education.


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## Body

## 1. Introduction

Music is one of the oldest, most universal, and infectious art forms of human beings. It is a special language for human beings to express their thoughts and feelings and realize mutual communication through the harmonious and orderly arrangement and combination of various sounds [1]. The creation, performance, understanding, and appreciation of music is one of the most basic spiritual activities of human beings. As the most important carrier of human culture, music has rich cultural and historical connotations, so it has been passed down for thousands of years and still occupies an indispensable position in human life. In the new era, its meaning, form of existence, and mode of communication have been reinterpreted with the rapid development of high technology [2].Artificial intelligence technology, also known as AI, is a brand-new technical science for the simulation, development, expansion, and extension of human intelligence. It involves many disciplines and may be applied in the field of music education in real life and become the future trend of music education. The attempts of artificial intelligence technology in music education are making breakthrough progress, which has had an impact on the teaching concepts, teaching methods, and teaching methods of music education. This article focuses on artificial intelligence [3]. The application in the field of music education and the analysis of the future development trend of artificial intelligence in music education will bring music education to a new height [4].In recent years, the active attempts and breakthrough progress of artificial intelligence in music application and music education are also amazing. The rapid development of contemporary scientific and technological means has brought about changes in the material and technical aspects of music teaching, which has played an important role in promoting the innovation and development of educators’ teaching models and teaching theories and has contributed to the establishment of different theories of modern music education and the development of new teaching concepts [5]. Formation, the update and improvement of teaching methods, teaching methods, and methods have brought positive changes and influences, and this is also the challenge and impact of technology to us. In such a huge and constantly developing market, a problem has gradually emerged, that is, resource providers cannot meet the needs of users (or even the providers themselves) for personalized and accurate retrieval of massive music data. Therefore, this paper combines artificial intelligence and music education to study technologies that intelligently and automatically process music and make music or music-related information easier to find. The purpose is also to understand artificial intelligence for others based on the current international, and domestic development status provides reference for this aspect of music education [6].


## 2. The Latest Technology

### 2.1. Music Element Analysis Technology Based on Music Computing

Influenced by the earlier research and development of speech signal processing technology, most of the current analysis techniques for music elements still follow the ideas and technical framework of speech signal processing, so the most mainstream research methods in this field are still based on statistical pattern recognition theory. Here, we will provide an overview of such methods from the basic methods and several hot research directions [7]. Similar to the speech signal processing technology, the typical process of music element analysis based on statistical methods includes extracting the statistical features of the music signal, decoding the features through a statistical model, and then, identifying or matching, to obtain a certain element of the music signal. The results of the attribute analysis are shown in Figure 1.Figure 1
Typical flow of music element analysis based on statistical methods.Compared with the feature extraction stage, the current music element analysis technology mainly uses some classic statistical models in the field of speech signal processing in the feature recognition and matching stage, which are summarized in Table1.Table 1
Overview of statistical models for feature recognition of music element analysis techniques.
Model typeModel instanceModel featuresModel methodState modelHidden Markov model (HMM)Fine spectrum timing structureHMM is trained by feature set, mapsn-dimensional features to a state, and classifies homogeneous featuresProbability distribution modelSingle/mixed Gaussian model (GMM)Global statisticsBuild a probability distribution model for a feature and use global statistical methods to identify the featureSequence modelTime seriesTiming features sequenceUse the corresponding relationship between the time series feature sequence and the original music to directly describe a certain attribute of the original music on a larger time scale


### 2.2. Music Element Analysis Technology Based on Statistical Methods

Methods based on statistical pattern recognition are widely used in music element analysis technology, and research work based on statistical methods can be found in almost all relevant subfields. Here, we select key areas for a brief introduction.


#### 2.2.1. Melody Line Estimation

The melody of music is formed by the orderly arrangement of tones of different pitches horizontally at a certain rhythm [8]. It runs through each section and is one of the most basic and important means of expression in a complete musical form. At the same time, it is also the most intuitive attribute of many musical elements. Classical literature points out that the existence of melody enables the listener to distinguish two different musical works. In part, a melody is a tune in a musical piece that we will remember for a long time and that we may still recall when we forget most of the piece [9].The most representative method for melody line estimation is the PreFEst method proposed by Goto. The PreFEst method considers all possible frequencies of F0 in the preset frequency region at each moment (the middle and high-pitched region, that is, the possible region where the melody of general musical works appears) and assumes that the input mixed sound to be analyzed contains the frequency of F0 [10]. All possible values, and these possible frequencies have corresponding weights according to different amplitudes. In this case, the PreFEst method represents the mixed sound to be analyzed using a weighted probability density function that represents a weighted mixture of the probability density functions (sound models) of all possible values of F0 [11]. Then, this method uses the maximum a posteriori probability estimation method (MAP) and the expectation maximization algorithm (EM) to estimate the weight of each possible value of F0 and its probability density function and select the possible value of F0 with the largest weight as the most significant value at this moment. Baseband: finally, this method also gives an algorithm for smoothing F0 so that it is continuous in time. The whole process is shown in Figure 2 [12].Figure 2
PreFEst algorithm flow.


#### 2.2.2. Analysis of Music Structure

Music structure analysis is the coarsest segmentation in music signal analysis (relative to onset-based segmentation and beat-based segmentation). Similarity of musical compositions is very helpful [13].Because of the diversity of musical structures, different people have different judgments on the division of musical structures. For example, Peeters proposed to consider structural analysis at different levels. Bartsch proposed a music summarization algorithm based on PCP feature representation. He proposed an algorithm for extracting chorus in music earlier and achieved good results in the popular music library with typical music structure [14]. Goto researches and improves Bartsch’s method, finds the repeats in the music by analyzing the relationship between various types of repeats, and then, identifies the repeats that are transposed. The chorus was detected on the self-built test set and achieved 80% of the detection rate, but this work is limited to the structural analysis of the chorus. Logan uses key phrase extraction technology to study music abstracts, extracts phrase features frame by frame and clusters them, uses this result to train the ergodic HMM of the target music to discover the song structure, and then, determines key phrases based on semantics [15]. The method finds key phrases on a test set of 18 Beatles’ pop music that performs better than HMM. Foote constructed a frame-by-frame comparison of similarity matrix to measure the degree of similarity between segments and finally used image processing to process the subdiagonals of the matrix to find similar segments [16].


### 2.3. Music Element Analysis Technology Based on Music Theory Cognition

In the field of MIR, research on music element analysis techniques is driven by the needs of retrieval tasks to determine the overall theoretical framework, core research questions, and the interrelationships between subfields. In contrast, the music element analysis technology based on music theory cognition organizes its research objects on the basis of music theory [17].In 1982, Krumhansl’s pioneering work on the relationship between notes, chords, and modes laid the foundation for the interpenetration and application of music cognitive science and computational science and played an important role in the proposal of many subsequent music cognitive computational models [18] with the role of demonstration. Krumhansl proposed the famous music psychology experiment “probe-tone”—first, design an incomplete mode scale (expansion experiment also involves chord sequence), and play it immediately after playing the adjacent next octave Then, use the hearing sense of music professionals (the extended experiment selects people without music professional training) to score whether the latter can fit the incomplete mode of the former and then form this mode profile (key profile), as shown in Figure 3 [19].Figure 3
Mode profiles of C major and A minor produced by scale detection technique.Due to the careful design of the probe-tone experiment, the interference of various subjective and objective factors such as the prior knowledge of traditional music theory, absolute pitch, and random scoring is removed, and a relatively accurate mode profile is obtained for use [20]. Further, Krumhansl performs correlation calculation on all mode profile combinations to determine the distances between modes and uses the numerical distance to build a torus model for all modes in four-dimensional space, as shown in Figure 4.Figure 4
Helical array music model proposed by Chew.While most music psychologists are devoted to the study of the relationship between pitch, chord, and mode and strive to solve the human cognitive nature of music, there are also some works on the relationship between pitch and melody. In addition to the aforementioned work of Terhardt, Longuet-Higgins carried out early work on melody line estimation, which did not use complex acoustic and computational models, but represented a note as a, the triplet composed of the end point of the note, and calculate the sharpness between each note to determine its position on the fifth circle, and finally, use the rules to determine its specific octave, and finally, form a melody line [21].


## 2.1. Music Element Analysis Technology Based on Music Computing

Influenced by the earlier research and development of speech signal processing technology, most of the current analysis techniques for music elements still follow the ideas and technical framework of speech signal processing, so the most mainstream research methods in this field are still based on statistical pattern recognition theory. Here, we will provide an overview of such methods from the basic methods and several hot research directions [7]. Similar to the speech signal processing technology, the typical process of music element analysis based on statistical methods includes extracting the statistical features of the music signal, decoding the features through a statistical model, and then, identifying or matching, to obtain a certain element of the music signal. The results of the attribute analysis are shown in Figure 1.Figure 1
Typical flow of music element analysis based on statistical methods.Compared with the feature extraction stage, the current music element analysis technology mainly uses some classic statistical models in the field of speech signal processing in the feature recognition and matching stage, which are summarized in Table1.Table 1
Overview of statistical models for feature recognition of music element analysis techniques.
Model typeModel instanceModel featuresModel methodState modelHidden Markov model (HMM)Fine spectrum timing structureHMM is trained by feature set, mapsn-dimensional features to a state, and classifies homogeneous featuresProbability distribution modelSingle/mixed Gaussian model (GMM)Global statisticsBuild a probability distribution model for a feature and use global statistical methods to identify the featureSequence modelTime seriesTiming features sequenceUse the corresponding relationship between the time series feature sequence and the original music to directly describe a certain attribute of the original music on a larger time scale


## 2.2. Music Element Analysis Technology Based on Statistical Methods

Methods based on statistical pattern recognition are widely used in music element analysis technology, and research work based on statistical methods can be found in almost all relevant subfields. Here, we select key areas for a brief introduction.


### 2.2.1. Melody Line Estimation

The melody of music is formed by the orderly arrangement of tones of different pitches horizontally at a certain rhythm [8]. It runs through each section and is one of the most basic and important means of expression in a complete musical form. At the same time, it is also the most intuitive attribute of many musical elements. Classical literature points out that the existence of melody enables the listener to distinguish two different musical works. In part, a melody is a tune in a musical piece that we will remember for a long time and that we may still recall when we forget most of the piece [9].The most representative method for melody line estimation is the PreFEst method proposed by Goto. The PreFEst method considers all possible frequencies of F0 in the preset frequency region at each moment (the middle and high-pitched region, that is, the possible region where the melody of general musical works appears) and assumes that the input mixed sound to be analyzed contains the frequency of F0 [10]. All possible values, and these possible frequencies have corresponding weights according to different amplitudes. In this case, the PreFEst method represents the mixed sound to be analyzed using a weighted probability density function that represents a weighted mixture of the probability density functions (sound models) of all possible values of F0 [11]. Then, this method uses the maximum a posteriori probability estimation method (MAP) and the expectation maximization algorithm (EM) to estimate the weight of each possible value of F0 and its probability density function and select the possible value of F0 with the largest weight as the most significant value at this moment. Baseband: finally, this method also gives an algorithm for smoothing F0 so that it is continuous in time. The whole process is shown in Figure 2 [12].Figure 2
PreFEst algorithm flow.


### 2.2.2. Analysis of Music Structure

Music structure analysis is the coarsest segmentation in music signal analysis (relative to onset-based segmentation and beat-based segmentation). Similarity of musical compositions is very helpful [13].Because of the diversity of musical structures, different people have different judgments on the division of musical structures. For example, Peeters proposed to consider structural analysis at different levels. Bartsch proposed a music summarization algorithm based on PCP feature representation. He proposed an algorithm for extracting chorus in music earlier and achieved good results in the popular music library with typical music structure [14]. Goto researches and improves Bartsch’s method, finds the repeats in the music by analyzing the relationship between various types of repeats, and then, identifies the repeats that are transposed. The chorus was detected on the self-built test set and achieved 80% of the detection rate, but this work is limited to the structural analysis of the chorus. Logan uses key phrase extraction technology to study music abstracts, extracts phrase features frame by frame and clusters them, uses this result to train the ergodic HMM of the target music to discover the song structure, and then, determines key phrases based on semantics [15]. The method finds key phrases on a test set of 18 Beatles’ pop music that performs better than HMM. Foote constructed a frame-by-frame comparison of similarity matrix to measure the degree of similarity between segments and finally used image processing to process the subdiagonals of the matrix to find similar segments [16].


## 2.2.1. Melody Line Estimation

The melody of music is formed by the orderly arrangement of tones of different pitches horizontally at a certain rhythm [8]. It runs through each section and is one of the most basic and important means of expression in a complete musical form. At the same time, it is also the most intuitive attribute of many musical elements. Classical literature points out that the existence of melody enables the listener to distinguish two different musical works. In part, a melody is a tune in a musical piece that we will remember for a long time and that we may still recall when we forget most of the piece [9].The most representative method for melody line estimation is the PreFEst method proposed by Goto. The PreFEst method considers all possible frequencies of F0 in the preset frequency region at each moment (the middle and high-pitched region, that is, the possible region where the melody of general musical works appears) and assumes that the input mixed sound to be analyzed contains the frequency of F0 [10]. All possible values, and these possible frequencies have corresponding weights according to different amplitudes. In this case, the PreFEst method represents the mixed sound to be analyzed using a weighted probability density function that represents a weighted mixture of the probability density functions (sound models) of all possible values of F0 [11]. Then, this method uses the maximum a posteriori probability estimation method (MAP) and the expectation maximization algorithm (EM) to estimate the weight of each possible value of F0 and its probability density function and select the possible value of F0 with the largest weight as the most significant value at this moment. Baseband: finally, this method also gives an algorithm for smoothing F0 so that it is continuous in time. The whole process is shown in Figure 2 [12].Figure 2
PreFEst algorithm flow.


## 2.2.2. Analysis of Music Structure

Music structure analysis is the coarsest segmentation in music signal analysis (relative to onset-based segmentation and beat-based segmentation). Similarity of musical compositions is very helpful [13].Because of the diversity of musical structures, different people have different judgments on the division of musical structures. For example, Peeters proposed to consider structural analysis at different levels. Bartsch proposed a music summarization algorithm based on PCP feature representation. He proposed an algorithm for extracting chorus in music earlier and achieved good results in the popular music library with typical music structure [14]. Goto researches and improves Bartsch’s method, finds the repeats in the music by analyzing the relationship between various types of repeats, and then, identifies the repeats that are transposed. The chorus was detected on the self-built test set and achieved 80% of the detection rate, but this work is limited to the structural analysis of the chorus. Logan uses key phrase extraction technology to study music abstracts, extracts phrase features frame by frame and clusters them, uses this result to train the ergodic HMM of the target music to discover the song structure, and then, determines key phrases based on semantics [15]. The method finds key phrases on a test set of 18 Beatles’ pop music that performs better than HMM. Foote constructed a frame-by-frame comparison of similarity matrix to measure the degree of similarity between segments and finally used image processing to process the subdiagonals of the matrix to find similar segments [16].


## 2.3. Music Element Analysis Technology Based on Music Theory Cognition

In the field of MIR, research on music element analysis techniques is driven by the needs of retrieval tasks to determine the overall theoretical framework, core research questions, and the interrelationships between subfields. In contrast, the music element analysis technology based on music theory cognition organizes its research objects on the basis of music theory [17].In 1982, Krumhansl’s pioneering work on the relationship between notes, chords, and modes laid the foundation for the interpenetration and application of music cognitive science and computational science and played an important role in the proposal of many subsequent music cognitive computational models [18] with the role of demonstration. Krumhansl proposed the famous music psychology experiment “probe-tone”—first, design an incomplete mode scale (expansion experiment also involves chord sequence), and play it immediately after playing the adjacent next octave Then, use the hearing sense of music professionals (the extended experiment selects people without music professional training) to score whether the latter can fit the incomplete mode of the former and then form this mode profile (key profile), as shown in Figure 3 [19].Figure 3
Mode profiles of C major and A minor produced by scale detection technique.Due to the careful design of the probe-tone experiment, the interference of various subjective and objective factors such as the prior knowledge of traditional music theory, absolute pitch, and random scoring is removed, and a relatively accurate mode profile is obtained for use [20]. Further, Krumhansl performs correlation calculation on all mode profile combinations to determine the distances between modes and uses the numerical distance to build a torus model for all modes in four-dimensional space, as shown in Figure 4.Figure 4
Helical array music model proposed by Chew.While most music psychologists are devoted to the study of the relationship between pitch, chord, and mode and strive to solve the human cognitive nature of music, there are also some works on the relationship between pitch and melody. In addition to the aforementioned work of Terhardt, Longuet-Higgins carried out early work on melody line estimation, which did not use complex acoustic and computational models, but represented a note as a, the triplet composed of the end point of the note, and calculate the sharpness between each note to determine its position on the fifth circle, and finally, use the rules to determine its specific octave, and finally, form a melody line [21].


## 3. Music Signal Transformation and Note Time Information Analysis

### 3.1. Introduction to the Basic Theory of Music Signal

At present, in the Western twelve well-tempered music theory system, which occupies an absolute dominant position in the music industry, there are a total of twelve scale roll calls: C, C#/Db, D, D#/Eb, E, F, F#/Gb, G, G#/Ab, A, A#/Bb, and B. In the musical score, each musical note has a roll name, but the notes with the same roll name belong to different octaves with different frequencies. Every twelve adjacent scales with different roll names form an octave, and all roll names in each octave appear in turn from a certain roll call, that is, the roll call is along the direction of the interval (frequency) increasing repeatedly looping in octaves. The physical frequencies of two scales separated by an octave are doubled, that is, ifX∗ is a scale with the same roll name as X and an octave higher than it, let fX∗,fX be its frequencies, but
(1)fx∗=2fx.Since the interval between every two adjacent scales in the same octave is a semitone, that is, the physical frequency ratio of every two adjacent scales is the same, it may be set asss>1, then have:
(2)fc∗=sfB=s2fA#/Bb=s3fA=⋯=s12fc.Simultaneously (2), there are:
(3)s12=2⇒s=212.From (3), we can know the quantitative relationship between the frequencies of two scales that differ by one semitone. Further, each semitone can be divided into smaller interval units—cents. Each semitone can be divided into 100 cents, that is, an octave has a total of 1200 cents. In the same way, we can know that the ratio of the frequencies of two tones differing by one cent is set as cc>1, then:
(4)s=c100⇒c=21/1200.Table2 is based on the standard scale pitch frequency comparison table, from which we can see the relationship between the roll call, octave, and the corresponding physical frequency of the standard scale. The gray part is the area where the melody part often appears.Table 2
Scientific pitch notation method and corresponding frequency table (part of diatonic scale).
Octave roll callFrequency/Hz (relative tone fraction, with C0 as the standard)0123456789C16.352 (0)32.703 (1200)65.406 (2400)130.81 (3600)261.63 (4800)523.25 (6000)1046.5 (7200)2093.0 (8400)4186.0 (9600)8372.0 (10800)D18.354 (200)36.708 (1400)73.416 (2600)146.83 (3800)293.66 (5000)587.33 (6200)1174.7 (7400)2349.3 (8600)4698.6 (9800)9397.3 (11000)E20.602 (400)41.203 (1600)82.407 (2800)164.81 (4000)329.63 (5200)659.26 (6400)1318.5 (7600)2637.0 (8800)5274.0 (10000)10548 (11200)F21.827 (500)43.654 (1700)87.307 (2900)174.61 (4100)349.23 (5300)698.46 (6500)1396.9 (7700)2793.8 (8900)5587.7 (10100)11175 (11300)G24.500 (700)48.999 (1900)97.999 (3100)196.00 (4300)392.00 (5500)783.99 (6700)1568.0 (7900)3136.0 (9100)6271.9 (10300)12544 (11500)A27.500 (900)55.000 (2100)110.00 (3300)220.00 (4500)440.00 (5700)880.00 (6900)1760.0 (8100)3520.0 (9300)7040.0 (10500)14080 (11700)B30.868 (11000)61.735 (2300)123.47 (3500)246.94 (4700)493.88 (5900)987.77 (7100)1975.5 (8300)3951.1 (9500)7902.1 (10700)15804 (11900)In the sound of actual music, due to the physical conditions of the sounding instrument, the sound produced by each note is polyphonic (that is, it has a harmonic structure, including many pure-tone sine wave components, which can be decomposed by Fourier analysis), but each polyphony both have a fundamental frequency. Here, we use frequency sequence to represent the harmonic structure of polyphony. If the fundamental frequency of a polyphony isf1, according to the classical theory of Fourier analysis, its harmonic structure can be expressed as:
(5)1f1;2f1;3f1;4f1;⋯;nf1.Perfect harmony—zero interval (unison), octave: it is obvious that the zero interval means that any musical tone is in harmony with its own sense of hearing. For octaves, according to (2) and (5), we can express the harmonic structure of two tones that differ by an octave as follows:
(6)1fx;2fx;3fx;⋯;nfx,1fx∗;2fx∗;3fx∗;⋯;nfx∗=2fx;4fx;6fx;⋯;2nfx.It can be known from (2-6) that for two tones that differ by one octave, the corresponding harmonic frequencies are both different from the integer multiples of the fundamental frequency of the lower pitch.Very harmonious—perfect fifth, perfect fourth: according to classical music theory, the ratios of fundamental frequencies of tones with a difference of a perfect fifth and a perfect fourth are 3/2 and 4/3, respectively, and their harmonic structures are shown in (2-7) and (2-8), respectively:(7)fx;2fx;3fx;⋯;nfx,fx∗;2fx∗;3fx∗;⋯;nfx∗=32fx;3fx;92fx;⋯;3n2fx,fx;2fx;3fx;⋯;nfx,fx∗;2fx∗;3fx∗;⋯;nfx∗=43fx;83fx;4fx;⋯;4n3fx.According to (2-7), the harmonic frequencies corresponding to musical tones that differ by a perfect fifth differ by an integer multiple offX/2. Since half of the harmonics differ in frequency not being an integer multiple of the fundamental frequency, they “destroy” the harmonics created by the other harmonics, so the resulting harmony does not seem to be as perfect as an octave. According to (2-8), the harmonic frequencies corresponding to the musical tones differing from a pure fourth differ by an integer multiple of fX/3. Compared with the pure fifth analysis, the harmonic components that “destroy” the harmony are more, so it is only the second imperfect harmonic intervals in perfect fifths.


### 3.2. ConstantQ-Transform Technique

As can be seen from the previous section, in the mainstream twelve equal-tempered temperament system in the West, there are 12 scales in each octave arranged in ascending order at semitone intervals, and the ratio of the physical frequencies of each adjacent two scales is constant. The distance of each semitone interval can be more accurately divided into 100 cents, and the frequency ratio of two notes with the same name that differ by 1 octave is 2 : 1, then the frequency ratio of two notes that differ by one semitone interval is 2 : 1. The frequency ratio of two adjacent notes is 1212. In this case, it is obvious that the frequencies of each scale (or even cents) are not equally spaced, but distributed in the form of geometric progression, and the scale distribution in the unit frequency range of the bass region is compared with that of the treble zones are more compact.In this paper, the constantQ transform (CQT) proposed in [98, 99] is adopted in the time-frequency transform stage when processing music audio signals. CQT is a transform whose frequency-to-bandwidth ratio is constant. It is designed according to the characteristics of music signals in the frequency domain and has many basic characteristics of DFT. Its definition is as follows (2-9)~(2-12):
(8)fk=fmin·2k/b,where fk is the kth frequency component (Hz) of the music signal in the transformed spectrum; fmin is the lower limit of the frequency of the processed music signal (Hz); and b is the number of spectral lines contained in an octave frequency range.Note that the frequency-to-bandwidth ratio isQ, then Q is a constant determined by b, CQT is also named because it keeps Q constant, and Q satisfies:
(9)Q=fδf=21/b−1−1,where δf is the bandwidth at frequency f (defined in DFT as the ratio of sampling rate to window length), also known as frequency resolution.According to the concept of frequency resolution, we can get:(10)Nk=Sδfk=S·Qfk,where Nk is the window length that varies with frequency and S is sampling rate.Finally, we follow the calculation method of the corresponding frequency component in DFT, and thekth component of CQT can be obtained as:
(11)xk=1Nk∑n=0Nk−1wNkn·xn·e−j2πnQ/Nkwhere xn is the time domain signal and WNkn is the window function of length Nk.It can be seen from (2-9)~(2-12) that although CQT has many similarities with DFT, CQT adopts a proportional sequence with a constant frequency bandwidth ratio, which sets frequency components according to the pitch relationship between notes, which is different from the traditional method of equal frequency spacing. CQT provides a more reasonable spectral representation for music signals in the frequency domain, especially on issues related to the pitch frequency of music, so that it can better describe the sound of music without conflicting with the musical expression, harmonic characteristics.


### 3.3. Note Onset Detection Technology

This section will introduce the note onset detection technology, which is an improvement of the classic method, used to obtain the time position information of the musical chord sequence, and is often used in various music element recognition technologies.An efficient detection function is constructed on the CQT spectrum, and the peak of the detection function is used to determine the position of the note onset. The core problem of this method is the construction of the detection function—the detection function should use a lower sampling rate in the calculation to reduce the amount of calculation and give a peak value when the starting point is encountered in the detection. Here, the onset detection function construction method proposed in this paper applies the idea of joint discrimination of amplitude and phase. Amplitude-based discriminant methods are very sensitive to the onset of tones with drastic changes in amplitude, similar to percussion sounds, while phase-based discriminants emphasize the detection of tonal onsets with “tonality.” The two methods are complementary, and the spectral features they require can be described and processed by similar statistical methods. To do this, we will construct the detection function in the complex domain. The spectral valuext,k of the CQT spectrum at time t and line k can be written in complex form:
(12)xt,k=xt,k·ejψt,k,where ψk,t is <−πψπk and t is the phase of xt,k.Then, we construct the detection functionDFt as follows:
(13)DFt=∑k=0Nb−1xt,k·ejψt,k−xt−1,k·ej2ψt−1,k−ψt−2,k.In the peak selection stage, we design an adaptive thresholdδt, on the basis of a basic threshold, use the weighted median within a certain range to filter out the real peak from the peak candidates in DFt; the moment corresponding to this peak is the start point of the note. Definition:
(14)δt=δ0+λ·MedianDFt−t1,⋯,DFt+t2,where t1, t2, δ0, and λ are the variable constants and Median is a function that takes the median value in a sequence.Here, the median selection range ofDFt at time t is t1+t2+1, and according to our experiments, t1=6, t2=2, δ0, and λ are constants. Obviously, the former is for δt and peak selection results. The impact is greater, and we set δ0 and λ to be 5.9 and 1, respectively, according to experimental debugging.


## 3.1. Introduction to the Basic Theory of Music Signal

At present, in the Western twelve well-tempered music theory system, which occupies an absolute dominant position in the music industry, there are a total of twelve scale roll calls: C, C#/Db, D, D#/Eb, E, F, F#/Gb, G, G#/Ab, A, A#/Bb, and B. In the musical score, each musical note has a roll name, but the notes with the same roll name belong to different octaves with different frequencies. Every twelve adjacent scales with different roll names form an octave, and all roll names in each octave appear in turn from a certain roll call, that is, the roll call is along the direction of the interval (frequency) increasing repeatedly looping in octaves. The physical frequencies of two scales separated by an octave are doubled, that is, ifX∗ is a scale with the same roll name as X and an octave higher than it, let fX∗,fX be its frequencies, but
(1)fx∗=2fx.Since the interval between every two adjacent scales in the same octave is a semitone, that is, the physical frequency ratio of every two adjacent scales is the same, it may be set asss>1, then have:
(2)fc∗=sfB=s2fA#/Bb=s3fA=⋯=s12fc.Simultaneously (2), there are:
(3)s12=2⇒s=212.From (3), we can know the quantitative relationship between the frequencies of two scales that differ by one semitone. Further, each semitone can be divided into smaller interval units—cents. Each semitone can be divided into 100 cents, that is, an octave has a total of 1200 cents. In the same way, we can know that the ratio of the frequencies of two tones differing by one cent is set as cc>1, then:
(4)s=c100⇒c=21/1200.Table2 is based on the standard scale pitch frequency comparison table, from which we can see the relationship between the roll call, octave, and the corresponding physical frequency of the standard scale. The gray part is the area where the melody part often appears.Table 2
Scientific pitch notation method and corresponding frequency table (part of diatonic scale).
Octave roll callFrequency/Hz (relative tone fraction, with C0 as the standard)0123456789C16.352 (0)32.703 (1200)65.406 (2400)130.81 (3600)261.63 (4800)523.25 (6000)1046.5 (7200)2093.0 (8400)4186.0 (9600)8372.0 (10800)D18.354 (200)36.708 (1400)73.416 (2600)146.83 (3800)293.66 (5000)587.33 (6200)1174.7 (7400)2349.3 (8600)4698.6 (9800)9397.3 (11000)E20.602 (400)41.203 (1600)82.407 (2800)164.81 (4000)329.63 (5200)659.26 (6400)1318.5 (7600)2637.0 (8800)5274.0 (10000)10548 (11200)F21.827 (500)43.654 (1700)87.307 (2900)174.61 (4100)349.23 (5300)698.46 (6500)1396.9 (7700)2793.8 (8900)5587.7 (10100)11175 (11300)G24.500 (700)48.999 (1900)97.999 (3100)196.00 (4300)392.00 (5500)783.99 (6700)1568.0 (7900)3136.0 (9100)6271.9 (10300)12544 (11500)A27.500 (900)55.000 (2100)110.00 (3300)220.00 (4500)440.00 (5700)880.00 (6900)1760.0 (8100)3520.0 (9300)7040.0 (10500)14080 (11700)B30.868 (11000)61.735 (2300)123.47 (3500)246.94 (4700)493.88 (5900)987.77 (7100)1975.5 (8300)3951.1 (9500)7902.1 (10700)15804 (11900)In the sound of actual music, due to the physical conditions of the sounding instrument, the sound produced by each note is polyphonic (that is, it has a harmonic structure, including many pure-tone sine wave components, which can be decomposed by Fourier analysis), but each polyphony both have a fundamental frequency. Here, we use frequency sequence to represent the harmonic structure of polyphony. If the fundamental frequency of a polyphony isf1, according to the classical theory of Fourier analysis, its harmonic structure can be expressed as:
(5)1f1;2f1;3f1;4f1;⋯;nf1.Perfect harmony—zero interval (unison), octave: it is obvious that the zero interval means that any musical tone is in harmony with its own sense of hearing. For octaves, according to (2) and (5), we can express the harmonic structure of two tones that differ by an octave as follows:
(6)1fx;2fx;3fx;⋯;nfx,1fx∗;2fx∗;3fx∗;⋯;nfx∗=2fx;4fx;6fx;⋯;2nfx.It can be known from (2-6) that for two tones that differ by one octave, the corresponding harmonic frequencies are both different from the integer multiples of the fundamental frequency of the lower pitch.Very harmonious—perfect fifth, perfect fourth: according to classical music theory, the ratios of fundamental frequencies of tones with a difference of a perfect fifth and a perfect fourth are 3/2 and 4/3, respectively, and their harmonic structures are shown in (2-7) and (2-8), respectively:(7)fx;2fx;3fx;⋯;nfx,fx∗;2fx∗;3fx∗;⋯;nfx∗=32fx;3fx;92fx;⋯;3n2fx,fx;2fx;3fx;⋯;nfx,fx∗;2fx∗;3fx∗;⋯;nfx∗=43fx;83fx;4fx;⋯;4n3fx.According to (2-7), the harmonic frequencies corresponding to musical tones that differ by a perfect fifth differ by an integer multiple offX/2. Since half of the harmonics differ in frequency not being an integer multiple of the fundamental frequency, they “destroy” the harmonics created by the other harmonics, so the resulting harmony does not seem to be as perfect as an octave. According to (2-8), the harmonic frequencies corresponding to the musical tones differing from a pure fourth differ by an integer multiple of fX/3. Compared with the pure fifth analysis, the harmonic components that “destroy” the harmony are more, so it is only the second imperfect harmonic intervals in perfect fifths.


## 3.2. ConstantQ-Transform Technique

As can be seen from the previous section, in the mainstream twelve equal-tempered temperament system in the West, there are 12 scales in each octave arranged in ascending order at semitone intervals, and the ratio of the physical frequencies of each adjacent two scales is constant. The distance of each semitone interval can be more accurately divided into 100 cents, and the frequency ratio of two notes with the same name that differ by 1 octave is 2 : 1, then the frequency ratio of two notes that differ by one semitone interval is 2 : 1. The frequency ratio of two adjacent notes is 1212. In this case, it is obvious that the frequencies of each scale (or even cents) are not equally spaced, but distributed in the form of geometric progression, and the scale distribution in the unit frequency range of the bass region is compared with that of the treble zones are more compact.In this paper, the constantQ transform (CQT) proposed in [98, 99] is adopted in the time-frequency transform stage when processing music audio signals. CQT is a transform whose frequency-to-bandwidth ratio is constant. It is designed according to the characteristics of music signals in the frequency domain and has many basic characteristics of DFT. Its definition is as follows (2-9)~(2-12):
(8)fk=fmin·2k/b,where fk is the kth frequency component (Hz) of the music signal in the transformed spectrum; fmin is the lower limit of the frequency of the processed music signal (Hz); and b is the number of spectral lines contained in an octave frequency range.Note that the frequency-to-bandwidth ratio isQ, then Q is a constant determined by b, CQT is also named because it keeps Q constant, and Q satisfies:
(9)Q=fδf=21/b−1−1,where δf is the bandwidth at frequency f (defined in DFT as the ratio of sampling rate to window length), also known as frequency resolution.According to the concept of frequency resolution, we can get:(10)Nk=Sδfk=S·Qfk,where Nk is the window length that varies with frequency and S is sampling rate.Finally, we follow the calculation method of the corresponding frequency component in DFT, and thekth component of CQT can be obtained as:
(11)xk=1Nk∑n=0Nk−1wNkn·xn·e−j2πnQ/Nkwhere xn is the time domain signal and WNkn is the window function of length Nk.It can be seen from (2-9)~(2-12) that although CQT has many similarities with DFT, CQT adopts a proportional sequence with a constant frequency bandwidth ratio, which sets frequency components according to the pitch relationship between notes, which is different from the traditional method of equal frequency spacing. CQT provides a more reasonable spectral representation for music signals in the frequency domain, especially on issues related to the pitch frequency of music, so that it can better describe the sound of music without conflicting with the musical expression, harmonic characteristics.


## 3.3. Note Onset Detection Technology

This section will introduce the note onset detection technology, which is an improvement of the classic method, used to obtain the time position information of the musical chord sequence, and is often used in various music element recognition technologies.An efficient detection function is constructed on the CQT spectrum, and the peak of the detection function is used to determine the position of the note onset. The core problem of this method is the construction of the detection function—the detection function should use a lower sampling rate in the calculation to reduce the amount of calculation and give a peak value when the starting point is encountered in the detection. Here, the onset detection function construction method proposed in this paper applies the idea of joint discrimination of amplitude and phase. Amplitude-based discriminant methods are very sensitive to the onset of tones with drastic changes in amplitude, similar to percussion sounds, while phase-based discriminants emphasize the detection of tonal onsets with “tonality.” The two methods are complementary, and the spectral features they require can be described and processed by similar statistical methods. To do this, we will construct the detection function in the complex domain. The spectral valuext,k of the CQT spectrum at time t and line k can be written in complex form:
(12)xt,k=xt,k·ejψt,k,where ψk,t is <−πψπk and t is the phase of xt,k.Then, we construct the detection functionDFt as follows:
(13)DFt=∑k=0Nb−1xt,k·ejψt,k−xt−1,k·ej2ψt−1,k−ψt−2,k.In the peak selection stage, we design an adaptive thresholdδt, on the basis of a basic threshold, use the weighted median within a certain range to filter out the real peak from the peak candidates in DFt; the moment corresponding to this peak is the start point of the note. Definition:
(14)δt=δ0+λ·MedianDFt−t1,⋯,DFt+t2,where t1, t2, δ0, and λ are the variable constants and Median is a function that takes the median value in a sequence.Here, the median selection range ofDFt at time t is t1+t2+1, and according to our experiments, t1=6, t2=2, δ0, and λ are constants. Obviously, the former is for δt and peak selection results. The impact is greater, and we set δ0 and λ to be 5.9 and 1, respectively, according to experimental debugging.


## 4. Experimental Results and Analysis

This paper has given the corresponding experiments on the performance of the techniques (CQT, pitch correction, and note onset detection technique) applied in the front-end of the musical tonal element analysis method based on the distributed characteristics of the pitch level. In this section, we will give detailed experiments on the various mechanisms of our proposed music element analysis method based on cognitive distributed features in the application background of mode recognition.


### 4.1. Experimental Analysis of Mode Detection Based on Cognitive Distribution Characteristics

First, comparative experiments are used to test the performance of the method for mode identification weighted on PCDM using fundamental frequency estimation, as shown in Table3. Here, the main indicators to measure the performance of the fundamental frequency estimation mechanism are the precision (pre), the recall rate (rec), and the accuracy (acc) of the musical tone detection points for the tone shift detection points of the musical segment.Table 3
Mode detection comparison experimental results with or without fundamental frequency estimation mechanism.
Our systemSystem without fundamental frequency weightingTotal music time (tmd) (s)6060.06060.0The total time to detect the correct mode (cdd) (s)4007.63854.3Number of transposition points (m)8383Number of transposition points detected (dm)179213Falsely detected transposition points (fpm)98144Shift point detection precision (pre) (%)45.2532.39Shift point detection recall rate (rec) (%)97.5983.13Mode detection accuracy (acc) (%)66.1363.60It can be seen from the above experiments that although the fundamental frequency detection technology cannot achieve an ideal recognition rate at present, applying it to the enhancement (weighting) of spectral features can still improve the modulation recognition method in this paper. The fundamental frequency weighted PCDM system has improved both in the identification of the pitch point and the identification of the overall mode. However, this improvement is not obvious. For example, the accuracy of transposition detection is still less than 50% after the improvement, while the accuracy of modulation detection is only improved by less than 3%. This fully shows that the low accuracy of the fundamental frequency estimation does limit the improvement of the fundamental frequency weighting mechanism to the system performance and also shows the necessity of researching a better fundamental frequency estimation method for the modulation detection method proposed in this paper.Next, a comparative test experiment was carried out on the role of the modulated smoothing postprocessing mechanism in the whole method, as shown in Table4. It can be seen from this set of comparative experiments that the proposed modulation smoothing postprocessing mechanism significantly improves the performance of modulation detection. Its function of maintaining the stability of the local mode reduces the number of falsely detected pitch points by more than 300%; at the same time, the smoothed out wrong pitch points also increases the overall mode recognition rate by 17%.Table 4
Mode detection comparison experiment results with or without debugging smoothing mechanism.
Our systemSystem without fundamental frequency weightingTotal music time (tmd) (s)6060.06060.0The total time to detect the correct mode (cdd) (s)4007.62997.1Number of transposition points (m)8383Number of transposition points detected (dm)179431Falsely detected transposition points (fpm)98355Shift point detection precision (pre) (%)45.2517.63Shift point detection recall rate (rec) (%)97.5991.57Mode detection accuracy (acc) (%)66.1349.13Through this set of experiments, it can be seen that in the case of continuous processing of audio without starting point detection, there are too many misidentified tone shift points, the recognition efficiency of the system is very poor, and the correct rate of tone recognition is also greatly reduced. For the system that manually adds the wrong starting point information, it can be seen that the wrong starting point information greatly reduces the accuracy of the pitch shift point detection, and at the same time, the modulation recognition rate also decreases a lot. It can be seen that the onset detection is very important for the system.


### 4.2. Experiments Related to Style Analysis

Music style can be effectively described by the sound quality and rhythm characteristics of music audio. According to this method, the short-term sound quality characteristics (MFCC and its first-order second-order difference and LPCC and its first-order second-order difference) are extracted, which are different from long-term sound quality characteristics. The prosody feature (beat histogram feature) of this section firstly verifies the classification effect of short-term sound quality features, then performs long-term and short-term feature fusion to classify the test data, and finally, gives a visual representation of music style analysis.Further, this paper still adopts the idea of “deliberately making mistakes,” using the method of manual labeling, increasing the proportion of misclassified chords, and reducing the success rate of chord primary selection from 38% to 30% and 25% manually. The results of the wrong chord primary selection are compared with the original system. For the calculation of the auditory saliency AS feature of the chromatic subband, as shown in Figure5, it is the variation pattern of each component of the saliency in different chromatic subbands when the first measure of the light music piece “Poeme” is played. We picked the 25th and 34th chromatic subbands as examples, which correspond to A4 and E5 in the scale, respectively. As can be seen from the music score in Figure 6, these two scales appear multiple times in chords, so the changes in the auditory saliency characteristics of their corresponding subbands are very representative.Figure 5
TANN network learning scale and precision.Figure 6
K-K mode profile of C major/minor scored by probe tone.The horizontal axis in Figure7 represents various musical styles, while avg represents the average recognition rate of all styles, and the vertical axis represents the recognition rate value. It can be seen here that the recognition effect using MFCC features is better than using LPCC features. Based on the assumption that the musical style is related to the sound quality and rhythm characteristics of the work, Figure 7 shows that MFCC and LPCC not only represent the sound quality characteristics of music, but their statistical extraction methods also make the characteristics themselves contain some information about the music rhythm characteristics; from the identification results, MFCC contains more such information than LPCC. The recognition rate obtained by the combination of two sound quality features is better than that obtained by using only one feature alone, which indicates that MFCC and LPCC have a certain degree of complementarity in the problem of music style classification. Therefore, when using the fusion of short-term sound quality features and long-term prosodic features for style classification, the 24-dimensional MFCC and LPCC mixed features are selected as the fusion of short-term features and prosodic features.Figure 7
Comparison of classification results based on three features.The mixing number of the GMM used in the above experiments is 16. This paper further calculates the influence of the Gaussian mixture numbers of 4, 8, 32, and 64 on the style classification results, as shown in Figures5–9. As can be seen from Figure 8, when the Gaussian mixture number is low, the style model obtained by training cannot adequately capture the differences between styles. When the Gaussian mixture number is large, due to the fixed size of the training data, the training of the style model may be saturated or even oversaturated, and the recognition rate cannot be significantly improved or even decreased. When the Gaussian mixture number increased from 4 to 32, the recognition rate of the classification methods based on the above three features increased, but from 32 to 64, the recognition rates of the three feature classification methods remained basically unchanged or decreased. When the Gaussian mixture number is from 16 to 32, the improvement of the recognition rate is very limited, but the resource cost and time consumption required for training and recognition will greatly increase, so the Gaussian mixture number of 16 is suitable in this experiment.Figure 8
Classification results of three features with different Gaussian mixture numbers.Next, the paper calculates the average likelihood ratio of each style classification result and represents it as a style vector and plots it in a radar chart. The average radar chart of the four styles classified using the mixed features of MFCC and LPCC is shown in Figure9: the average radar chart of rock style has a higher score for metal, while a similar effect can be observed in the radar chart of metal style, which illustrates the great similarity between rock and metal styles, which have always been classified as a branch of rock; jazz scores higher on the radar chart of classical styles, and the classical score is relatively high, and the metal score is the lowest in both, indicating that the two styles of classical and jazz are easy to confuse, and they are the most different from the metal style. The observations of the musical styles in the above radar charts are also very consistent with the actual human sense of listening to musical styles.Figure 9
Average radar chart representation of four styles. (a) Classical music radar chart. (b) Jazz music radar chart. (c) Radar chart of metal style music. (d) Radar chart of rock style music.
(a)(b)(c)(d)This paper studies the problem of music style classification. This paper proposes to use the mixture features of MFCC, LPCC, and beat histogram with Gaussian mixture model to classify music styles and conduct in-depth research on the extraction methods and fusion methods of these features. At the same time, this paper proposes to use the musical style vector to study the stylistic tendency of a musical piece, and the radar chart representation based on the style vector provides a visual analysis of the stylistic tendency—using the likelihood ratio calculated by the musical piece to be classified and the GMM-based style model established a music style vector and used the style vector to draw a radar chart to provide a visual aid for the analysis of the degree of confusion between different styles and the style tendency of dual-style music.


## 4.1. Experimental Analysis of Mode Detection Based on Cognitive Distribution Characteristics

First, comparative experiments are used to test the performance of the method for mode identification weighted on PCDM using fundamental frequency estimation, as shown in Table3. Here, the main indicators to measure the performance of the fundamental frequency estimation mechanism are the precision (pre), the recall rate (rec), and the accuracy (acc) of the musical tone detection points for the tone shift detection points of the musical segment.Table 3
Mode detection comparison experimental results with or without fundamental frequency estimation mechanism.
Our systemSystem without fundamental frequency weightingTotal music time (tmd) (s)6060.06060.0The total time to detect the correct mode (cdd) (s)4007.63854.3Number of transposition points (m)8383Number of transposition points detected (dm)179213Falsely detected transposition points (fpm)98144Shift point detection precision (pre) (%)45.2532.39Shift point detection recall rate (rec) (%)97.5983.13Mode detection accuracy (acc) (%)66.1363.60It can be seen from the above experiments that although the fundamental frequency detection technology cannot achieve an ideal recognition rate at present, applying it to the enhancement (weighting) of spectral features can still improve the modulation recognition method in this paper. The fundamental frequency weighted PCDM system has improved both in the identification of the pitch point and the identification of the overall mode. However, this improvement is not obvious. For example, the accuracy of transposition detection is still less than 50% after the improvement, while the accuracy of modulation detection is only improved by less than 3%. This fully shows that the low accuracy of the fundamental frequency estimation does limit the improvement of the fundamental frequency weighting mechanism to the system performance and also shows the necessity of researching a better fundamental frequency estimation method for the modulation detection method proposed in this paper.Next, a comparative test experiment was carried out on the role of the modulated smoothing postprocessing mechanism in the whole method, as shown in Table4. It can be seen from this set of comparative experiments that the proposed modulation smoothing postprocessing mechanism significantly improves the performance of modulation detection. Its function of maintaining the stability of the local mode reduces the number of falsely detected pitch points by more than 300%; at the same time, the smoothed out wrong pitch points also increases the overall mode recognition rate by 17%.Table 4
Mode detection comparison experiment results with or without debugging smoothing mechanism.
Our systemSystem without fundamental frequency weightingTotal music time (tmd) (s)6060.06060.0The total time to detect the correct mode (cdd) (s)4007.62997.1Number of transposition points (m)8383Number of transposition points detected (dm)179431Falsely detected transposition points (fpm)98355Shift point detection precision (pre) (%)45.2517.63Shift point detection recall rate (rec) (%)97.5991.57Mode detection accuracy (acc) (%)66.1349.13Through this set of experiments, it can be seen that in the case of continuous processing of audio without starting point detection, there are too many misidentified tone shift points, the recognition efficiency of the system is very poor, and the correct rate of tone recognition is also greatly reduced. For the system that manually adds the wrong starting point information, it can be seen that the wrong starting point information greatly reduces the accuracy of the pitch shift point detection, and at the same time, the modulation recognition rate also decreases a lot. It can be seen that the onset detection is very important for the system.


## 4.2. Experiments Related to Style Analysis

Music style can be effectively described by the sound quality and rhythm characteristics of music audio. According to this method, the short-term sound quality characteristics (MFCC and its first-order second-order difference and LPCC and its first-order second-order difference) are extracted, which are different from long-term sound quality characteristics. The prosody feature (beat histogram feature) of this section firstly verifies the classification effect of short-term sound quality features, then performs long-term and short-term feature fusion to classify the test data, and finally, gives a visual representation of music style analysis.Further, this paper still adopts the idea of “deliberately making mistakes,” using the method of manual labeling, increasing the proportion of misclassified chords, and reducing the success rate of chord primary selection from 38% to 30% and 25% manually. The results of the wrong chord primary selection are compared with the original system. For the calculation of the auditory saliency AS feature of the chromatic subband, as shown in Figure5, it is the variation pattern of each component of the saliency in different chromatic subbands when the first measure of the light music piece “Poeme” is played. We picked the 25th and 34th chromatic subbands as examples, which correspond to A4 and E5 in the scale, respectively. As can be seen from the music score in Figure 6, these two scales appear multiple times in chords, so the changes in the auditory saliency characteristics of their corresponding subbands are very representative.Figure 5
TANN network learning scale and precision.Figure 6
K-K mode profile of C major/minor scored by probe tone.The horizontal axis in Figure7 represents various musical styles, while avg represents the average recognition rate of all styles, and the vertical axis represents the recognition rate value. It can be seen here that the recognition effect using MFCC features is better than using LPCC features. Based on the assumption that the musical style is related to the sound quality and rhythm characteristics of the work, Figure 7 shows that MFCC and LPCC not only represent the sound quality characteristics of music, but their statistical extraction methods also make the characteristics themselves contain some information about the music rhythm characteristics; from the identification results, MFCC contains more such information than LPCC. The recognition rate obtained by the combination of two sound quality features is better than that obtained by using only one feature alone, which indicates that MFCC and LPCC have a certain degree of complementarity in the problem of music style classification. Therefore, when using the fusion of short-term sound quality features and long-term prosodic features for style classification, the 24-dimensional MFCC and LPCC mixed features are selected as the fusion of short-term features and prosodic features.Figure 7
Comparison of classification results based on three features.The mixing number of the GMM used in the above experiments is 16. This paper further calculates the influence of the Gaussian mixture numbers of 4, 8, 32, and 64 on the style classification results, as shown in Figures5–9. As can be seen from Figure 8, when the Gaussian mixture number is low, the style model obtained by training cannot adequately capture the differences between styles. When the Gaussian mixture number is large, due to the fixed size of the training data, the training of the style model may be saturated or even oversaturated, and the recognition rate cannot be significantly improved or even decreased. When the Gaussian mixture number increased from 4 to 32, the recognition rate of the classification methods based on the above three features increased, but from 32 to 64, the recognition rates of the three feature classification methods remained basically unchanged or decreased. When the Gaussian mixture number is from 16 to 32, the improvement of the recognition rate is very limited, but the resource cost and time consumption required for training and recognition will greatly increase, so the Gaussian mixture number of 16 is suitable in this experiment.Figure 8
Classification results of three features with different Gaussian mixture numbers.Next, the paper calculates the average likelihood ratio of each style classification result and represents it as a style vector and plots it in a radar chart. The average radar chart of the four styles classified using the mixed features of MFCC and LPCC is shown in Figure9: the average radar chart of rock style has a higher score for metal, while a similar effect can be observed in the radar chart of metal style, which illustrates the great similarity between rock and metal styles, which have always been classified as a branch of rock; jazz scores higher on the radar chart of classical styles, and the classical score is relatively high, and the metal score is the lowest in both, indicating that the two styles of classical and jazz are easy to confuse, and they are the most different from the metal style. The observations of the musical styles in the above radar charts are also very consistent with the actual human sense of listening to musical styles.Figure 9
Average radar chart representation of four styles. (a) Classical music radar chart. (b) Jazz music radar chart. (c) Radar chart of metal style music. (d) Radar chart of rock style music.
(a)(b)(c)(d)This paper studies the problem of music style classification. This paper proposes to use the mixture features of MFCC, LPCC, and beat histogram with Gaussian mixture model to classify music styles and conduct in-depth research on the extraction methods and fusion methods of these features. At the same time, this paper proposes to use the musical style vector to study the stylistic tendency of a musical piece, and the radar chart representation based on the style vector provides a visual analysis of the stylistic tendency—using the likelihood ratio calculated by the musical piece to be classified and the GMM-based style model established a music style vector and used the style vector to draw a radar chart to provide a visual aid for the analysis of the degree of confusion between different styles and the style tendency of dual-style music.


## 5. Conclusion

(1)
The rapid development and great changes brought by artificial intelligence technology to society are no longer comparable. Similarly, artificial intelligence technology will also affect the teaching of music education. This paper uses a lot of space to explain the possibility of applying artificial intelligence technology to music education. From a practical point of view, we can already use artificial intelligence technology to carry out music theory knowledge and relatively elementary music-assisted learning. Different from traditional music signal processing technology, this paper starts from music theory and adheres to the guidance of cognitive theory within the framework of music computing and establishes a novel intelligent method to analyze and identify music elements. According to the music computing system, this paper studies some music signal processing technologies, and under the guidance of the music computing technology system, the core music tonal attribute elements such as mode and chord, the main evolutionary form elements of music such as melody, and the music structure and style are studied. Global feature information is studied. Similarly, artificial intelligence technology in the education industry and music education industry is just around the corner. We must learn to live intelligently in an environment where science and technology are becoming more and more powerful(a)
The recognition effect using MFCC features is better than that using LPCC features. The recognition rate obtained by combining the two sound quality features is better than that obtained by using only one feature, which shows that MFCC and LPCC have certain complementarity in music style classification. Therefore, when using the fusion of short-term sound quality features and long-term prosodic features for style classification, 24 dimensional MFCC and LPCC mixed features are selected as the fusion of short-term features and prosodic features. When the Gaussian mixture number increases from 4 to 32, the recognition rate of the classification methods based on the above three features increases, but when it increases from 32 to 64, the recognition rate of the three feature classification methods basically remains unchanged or decreases. When the Gaussian mixture number is from 16 to 32, the improvement of recognition rate is very limited, but the resource cost and time consumption required for training and recognition will increase greatly, so the Gaussian mixture number 16 is suitable for this experiment



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*Source: 6766651-2022-07-29.xml*