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Parsimony is a broad concept with applications in music theory and composition. Two sets A and B of finite cardinality n (n-sets) are in parsimonious relation if there exists a (n–1)-set C that is included in both A and B. A sequence of n-sets is parsimonious if any two successive sets in the sequence are in parsimonious relation. This work demonstrates that for any set of finite cardinality p, there exist sequences of its n-subsets (n ≤ p) that are circular, non-redundant, exhaustive and parsimonious, and it describes the corresponding algorithm. The image of such a sequence by a bijection and the retrograde sequence keep the same four properties. The consequences of these results for the pitch class (pc) subsets of cardinality n (or n-chords) of a pc set of finite cardinality p (p-tonic scale) are derived and discussed in the context of music harmony, microtonality and composition. [ABSTRACT FROM AUTHOR]

By a scheme of a musical canon, we mean the time and pitch displacement of each entering voice. When the time displacements are unequal, achieving consonant sonorities is especially challenging. Using a first-species theoretical model, we quantify the flexibility of schemes that Renaissance composers used or could have used. We craft an algorithm to compute this flexibility value precisely (finding in the process that it is an algebraic integer). We find that Palestrina consistently selected some of the most flexible schemes, more so than his predecessors, but that he by no means exhausted all feasible schemes. To add support to the model, we present two new compositions within the limits of the style utilizing unexplored canonic schemes. In the Online Supplement, we provide MIDI realizations of the musical examples and Sage code used in the numerical computations. [ABSTRACT FROM AUTHOR]

We propose an approach to tuning systems in which octave doubling ratio is replaced by a suitable algebraic unit τ, and note frequencies are proportional to a subset of the ring $ \mathbb {Z}[\tau ] $ Z [ τ ]. Then it is possible for many difference tones between notes in the tuning to also appear in the tuning. After outlining more general principles, we consider in detail some natural examples based on the golden ratio $ \phi =(1+\sqrt {5})/2 $ ϕ = (1 + 5) / 2 , limited by norm or by the number of digits in the greedy β-expansion. We discuss additive and multiplicative properties, implementation and composition using these tunings. The Online Supplement contains MIDI and websynths files to implement the tuning $ S_\beta ^5(\phi) $ S β 5 (ϕ) (based on β-expansions to $ \phi ^{-5} $ ϕ − 5 ) on websynths.com and a composition Three Places. [ABSTRACT FROM AUTHOR]

We develop a novel method to represent a weighted directed graph as a finite metric space and then use persistent homology to extract useful features. We apply this method to weighted directed graphs obtained from pitch transitions information of a given musical fragment and use these techniques to the quantitative study of stylistic trends. As a first illustration, we analyse a selection of string quartets by Haydn, Mozart and Beethoven and discuss possible implications of our results in terms of different approaches by these composers to stylistic exploration and variety. We observe that Haydn is stylistically the most conservative, followed by Mozart, while Beethoven is the most innovative. Finally we also compare the variability of different genres, namely minuets, allegros, prestos, and adagios, by a given composer and conclude that the minuet is the most stable form of the string quartet movements. [ABSTRACT FROM AUTHOR]

In the present article we describe and discuss a framework for applying different topological data analysis (TDA) techniques to a music fragment given as a score in traditional Western notation. We first consider different sets of points in Euclidean spaces of different dimensions that correspond to musical events in the score, and obtain their persistent homology features. Then we introduce two families of simplicial complexes that can be associated with chord sequences, and leverage homology to compute their salient features. Finally, we show the results of applying the described methods to the analysis and stylistic comparison of fragments from three Brandenburg Concertos by J.S. Bach and two pieces from the Graffiti series by Mexican composer Armando Luna. [ABSTRACT FROM AUTHOR]

Common AI music composition algorithms train a machine by feeding a set of music pieces. This approach is a blackbox optimization, i.e. the underlying composition algorithm is, in general, unknown to users. In this paper, we present a method of machine composition that teaches a machine the compositional principles embedded in the music using the concept of overlap matrix. In (Tran Mai Lan, Changbom Park & Jae-Hun Jung (2023) Topological data analysis of Korean music in Jeongganbo: a cycle structure, Journal of Mathematics and Music, DOI: 10.1080/17459737.2022.2164626), a type of Korean music called Dodeuri music has been analysed using topological data analysis (TDA). To apply TDA, the music data is first reconstructed as a graph. Through TDA on the constructed graph, a unique set of cycles is found. The overlap matrix lets us visualize how those cycles are interconnected in music. We explain how we use the overlap matrix for machine composition. The overlap matrix is suitable for algorithmic composition and also provides seed music to train an artificial neural network. [ABSTRACT FROM AUTHOR]

This paper studies the resonant frequencies of three-string networks by examining the roots of the relevant spectral equation. A collection of scaling laws are established which relate the frequencies to structured changes in the lengths, densities, and tensions of the strings. Asymptotic properties of the system are derived, and several situations where transcritical bifurcations occur are detailed. Numerical optimization is used to solve the inverse problem (where a desired set of frequencies is specified and the parameters of the system are adjusted to best realize the specification). The intrinsic dissonance of the overtones provides an approximate way to measure the inherent inharmonicity of the sound. [ABSTRACT FROM AUTHOR]

The logarithm mapping of natural numbers is a sum of products of coefficients. If these coefficients are arbitrary parameters, a new mapping of natural numbers to some subset of real numbers appears. This mapping preserves some crucial logarithm properties and constructs a new musical sound with a spectrum of inharmonic overtones. The simplest one-parameter mapping to a subset of polynomials with integer coefficients is constructed. The parameter defines a new "perfect fifth" similarly to the meantone temperament. It is an interesting case when the mapping parameter is defined from the linear relation between the new "major third" and the new "perfect fifth." The most interesting case of a linear relation is the condition of zeroing the syntonic comma. Here, the new meantone temperament, based on the new "perfect fifth," simultaneously coincides with Pythagorean- and five-limit-like tunings. [ABSTRACT FROM AUTHOR]

We apply the minimum description length (MDL) methodology from information theory in order to select mathematical models for musical rhythm. We consider six models proposed by David Temperley and mathematically formalize them, which allows for an MDL analysis. As a consequence, we find that two of the models are not suitable to apply this methodology, as their codification strategy does not represent all possible rhythm sequences. A slight modification of Temperley's hierarchical model provides an improvement in codification performance, robust across all the four corpora considered in the experiments: three classical corpora commonly used in music studies and one of tango songs recently released. Our study confirms the usefulness of the MDL approach to solve the classical trade-off between model complexity and its ability to fit the data. [ABSTRACT FROM AUTHOR]

This paper constructs a blended teaching strategy based on Internet information technology and gives the teaching design of Wu Di music culture education. Behavioral science theory and the K-means clustering algorithm of rough set optimization were used as the guide to construct the behavioral analysis model of students’ learning Wu Di music culture education. The effectiveness of the blended teaching model in the inheritance and innovation of Wu Di music culture education was verified through empirical analysis. The results show that blended teaching can further enhance students’ absorption of Wudi music culture, and the percentage of the experimental class who are very interested in Wudi music culture education is 12.03% higher than that of the control class. The blended teaching mode can enhance students' interest in Wudi music and culture education and contribute to the inheritance and innovation of Wudi music and culture education.

This paper first determines the emotional, melodic, and structural characteristics through non-negative matrix decomposition and graph network theory to enhance students’ cognition and understanding of traditional music culture and to pass on and carry forward traditional music culture. Utilizing the RBF algorithm to integrate the elements of traditional music culture characteristics into efficient music teaching, forming a one-piece interactive teaching method. Utilizing the large database module for teaching and deep learning of learners can create a good college vocal music teaching loop. The student interest rate in traditional instrumental music teaching classes under the proposed method is increased to 98.98%, providing a solid foundation for integrating college vocal music teaching and traditional music culture.

This paper develops a cluster analysis model based on big data analytics, which first utilizes ETL tools to extract, clean, and standardize music teaching data from massive data. Then, the preprocessed music teaching data was analyzed using a big data technology platform. Finally, the clustering algorithm has been improved, and the K-means algorithm based on density optimization is designed to cluster the music teaching data. Using this model to analyze the learning behavior of college music classroom yields that the quantitative values of test activities and classroom performance in Classification 1 are only 11 and 14, the quantitative value of classroom performance in Classification 2 is only 9, and the quantitative value of homework group task in Classification 3 is only 9. The analysis shows that the quality of music teachers in colleges and universities is not uniform. The current state of music teaching in colleges and universities is poor, and it is imperative to create and improve music education.

This paper uses the ARIMA model in time series and the BLSTM sentiment classification algorithm in sentiment analysis to predict the elements and the direction of traditional music heritage. Differential processing of non-smooth series data stabilizes the time series data of traditional music inheritance. By extracting all the features contained in the sheet music, the interpreted sheet music is subjected to sentiment analysis to further analyze the inheritable elements of traditional music. The results show that the time series model has high accuracy in predicting the inheritable elements of traditional music, the MAPE value of the ARIMA model is 5.9658308, and the melody, as well as the structure of traditional music, can be integrated into piano teaching in colleges and universities to a certain extent, with the integration degree of the melody being 0.72 and the integration degree of the structure is 0.655.

The diffusion degree of traditional music in music teaching is analyzed by constructing an information diffusion model based on traditional music knowledge. Combine the information flow window to analyze the path process of integrating traditional music culture and music teaching. Combine the kernel function to calculate the degree of similarity between traditional music culture and music teaching. Analyze the students’ preference for traditional music culture content in music teaching by calculating the traditional music theme preference value. The results show that music teaching that incorporates traditional music culture has a better diffusion effect on teaching content, teaching method and teaching form, with the diffusion degree in the interval of [0.45, 0.85), and the diffusion degree interval of teaching information that does not incorporate traditional music culture is in the interval of [0.25,0.35). This study contributes to the integration of traditional music culture and music teaching to a certain extent and enhances the effect of music teaching in colleges and universities.

In order to innovate the college music education model, this paper first analyzes the design ideas of music-diversified courses based on deep learning and carries out the feature extraction of music course content. Secondly, combined with the diversified music wisdom environment and curriculum evaluation, a diversified music curriculum system teaching model based on deep learning is designed. Finally, the teaching practice is conducted, and a control experiment is designed to analyze the effectiveness of diversified curriculum innovations. The results showed that after the teaching practice, the basic level of the experimental class increased by 0.26, the cognitive ability increased by 0.23, the learning attitude increased by 0.23, the learning style increased by 0.23, and the interests and hobbies increased by 0.25. The diversified curriculum based on this paper can effectively carry out music teaching innovation.

Music content needs to draw nutrients from excellent regional culture, and excellent regional culture needs to be carried forward through music, so the integration of intercity culture and Xinjiang music is in line with the development and inheritance of music culture. This paper briefly introduces the analysis model of the SCP paradigm and the music recommendation algorithm improved by K-Means and Canopy clustering algorithms and analyzes the integration of intercity culture and Xinjiang music in the context of “One Belt One Road” based on the SCP model and the improved algorithm. The analysis shows that music lessons incorporating intercultural culture account for about 25% of the lesson time in Xinjiang’s primary and secondary school music curriculum, and in 2013, the content of Xinjiang’s local music culture at the junior high school level accounts for more than 35% of the total lesson time in the music curriculum. Intercultural culture and Xinjiang music are gradually merging, and music education will play a large role in further educational reform in terms of cultural transmission and integration.

To seek the path and method of innovation and reform of art practice of ethnic music education in colleges and universities. Based on information fusion technology, this paper introduces the Kalman filter algorithm for analysis and recognition of ethnic music education in colleges and universities and understands the application of information fusion technology in ethnic music education in colleges and universities through the correct recognition rate and recognition time of music signals. We also apply independent experiential and problem-based experiments to verify the feasibility of information fusion technology in innovative reform. The experimental results show that the Kalman filter algorithm based on information fusion technology has a 96.57% correct recognition rate for ethnic music signals, and the recognition time is faster than the support vector machine and BP neural network by 3.587s and 1.291s on average. 73.47% of the students, on average, think that the innovative reform of art practice based on information fusion technology in college ethnic music education is very effective, which is higher than the original The average percentage of students who thought the effect of the innovation reform of folk music education based on information integration technology was very good was 73.47%, which was 60.37 percentage points higher than the original research results. The average percentage of students who thought it was ineffective was only 9.19%, 52.31 percentage points lower than the original research results. The above results prove the feasibility of information fusion technology in reforming art practice innovation in university ethnomusicology education and provide a new direction for reforming art practice innovation in university ethnomusicology education.

In this paper, we first investigate the steps to implement a fusion algorithm that determines the quality function through fuzzy theory. Then, we utilize D-S evidence theory for decision-level fusion to identify the target data based on the collected data. The two algorithms, fuzzy theory and D-S evidence theory, are then combined. The affiliation function in fuzzy theory calculates the information collected from the sensors to find out the confidence level and patterns. Finally, the information fusion technology was analyzed in terms of its usage rate in music theory teaching, its impact on piano playing, and its impact on music theory teaching. In terms of utilization rate, 21 student teachers had information fusion technology utilization rate between 50% and 60%, and 21 student teachers had information fusion technology utilization rate between 50% and 60%. In terms of the impact of information fusion technology on the teaching of music theory, a comparative analysis of the pre and post test data T=−6.55 (P

People’s traditional music passed down from generation to generation should be heard by the world, and music tunes, as one of the indispensable basic elements of traditional music, need to be studied more deeply and systematically. In this paper, based on modern network information technology, we use big data visualization technology to analyze and study traditional music tunes. Using the RGB color space principle to understand the visualization technology, the number of research literature related to traditional music tunes in the past 20 years is obtained through literature collation, and the chart way of visualization using Sankey diagrams is analyzed to conclude traditional music tunes research. The data show that most research literature on traditional music tunes features an average of 6 articles per year. The number of ephemeral articles has gradually increased over the decade, with the total effective number of articles being about 7 in 2002 and increasing to about 39 in 2022. The research institutions are mainly concentrated in many universities in Henan, Yunnan, Hunan, Guangdong, and Guangxi, but the main traditional music tunes studied in each place also Have differences. The research on traditional music tunes by universities has a guiding and leading role, and the increase and depth of related research literature are also conducive to the protection and inheritance of traditional music tunes and integration and innovation in each region.

The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.

In this paper, during the design process, some sample data are first extracted, and the distance between data is calculated to obtain the symmetric distance matrix along the diagonal. The model mutual information quantity is introduced, and the homogenization factor is detected after weight calculation. Next, analyze the rule weights based on the normalized mutual information quantity and use information entropy to identify abnormal data. Finally, find the minimum set with frequent item set and mine the fuzzy association rules. To verify the feasibility of the model, the results of exploring the characteristics of ethnic music tunes show that the neutral tone color is thin and the ethnic music melody line is soft in tune when the long and short melody lines are 9% and 12%, respectively. The number of strong and weak beats in the beat is 2-7 times, mixed with 2/4 and 3/8 beats alternately, and the musical tune is regular. The third-order interaction of wave amplitude variance is significant, and the two media of CD and phonograph help listeners understand and remember the music. Based on this, the digital model can improve the reproduction effect of folk music and can objectively target the transmission of folk music with national cultural characteristics.

This paper uses a user-based collaborative filtering algorithm to predict the ratings of items by target users and to recommend items that target users have not interacted with. The red music and cultural resources were ranked according to their values, and the Bartholomew coefficient was introduced to calculate the similarity of items from the perspective of a probability distribution, and the content recommendation algorithm was used to construct a model of ecological civilization education concept. It is found that more than 50% of undergraduate college students pay regular attention to ecological environment information, and 30.56% of them will actively learn ecological civilization knowledge, which is not thorough and comprehensive enough for ecological civilization concept establishment and cultivation. It is suggested that universities should integrate red music culture into ecological civilization education to create an ecological civilization cultivation atmosphere.

In order to better promote the work of civic education, all other courses share the responsibility of civic education. Using information fusion technology to integrate cloud classrooms and rain classrooms, we construct a strategy to realize the fusion path of civic education and music teaching based on the catechism. The K-modes algorithm is improved based on the basic idea of k-means, and the expected effect of the fusion path is evaluated using the algorithm and the learning evaluation model based on the hierarchical analysis method. The results showed that the integration path resulted in more than 70% of teachers’ evaluations being qualified, students’ evaluation scores in music increasing from no more than 70 to more than 85, and the average rating of overall quality increasing from 66.98 to 77.45. It can be seen that the proposed pathway organically integrates aesthetic and moral education, which enables students to improve their artistic and moral qualities and achieve all-around development in the process of music teaching.

In order to explore the origin and significance of music education reform and talent cultivation in the new era, this paper constructs a new music curriculum evaluation model based on SVM. Firstly, we explore the path of music education reform and the goal of music talent cultivation by taking the trend of Internet of everything as the origin of the proposed “high-quality courses” for music in universities. Then, we analyzed that the recognition accuracy of SVM is significantly better than most machine learning methods in solving high-dimensional small sample data, so we established a new music curriculum teaching evaluation model on this basis. The average score of each index of the new course teaching effect is 74, and the evaluation grade is better, obtained by establishing the evaluation system of the new music course of SVM. In the evaluation index, the CVI indexes were all kept in the range of 0.82 to 0.98, which was higher than the standard 0.78 of the CVI index, and the Kappa value of each evaluation index was higher than 0.86. The attendance rate in college teaching rose from 78% to 100%, the ability to master music knowledge rose from 67% to 93%, the ability to apply knowledge rose from 76% to 92%, and the average score on weekly tests rose from 86 to 96. Finally, it is concluded that the Internet-based music education reform approach can promote the development of music education careers and cultivate the important means of all-around talents.

This paper proposes the use of the Hadoop-distributed file system HDFS as a storage system for large resources in music education. In order to improve the overall performance of HDFS for storing educational resources and to solve the deficiencies of the current system. This paper provides an overall analysis of the current state of research related to the placement algorithm and data storage strategy of HDFS, respectively. After combining the characteristics of educational resources themselves and establishing an educational resource value evaluation index system, an improved placement algorithm and a hybrid redundant storage strategy based on educational resource ratings are proposed, and the proposed algorithm or strategy is simulated and implemented and verified through source code analysis of HDFS-related algorithms. The results show that the average response time for uploading 10 songs is 7.734 s, and the average response time for image retrieval is 0.768 s. In the analysis of digital music education resources demand, teachers have the highest usage rate of music teaching theory (M=3.43), 38.45% of students’ demand is mainly for downloading music, and music lovers querying popular music has the highest percentage of 19.43%. The algorithm in this paper achieves the storage load balance of each node, which basically satisfies the demand for music education resources of teachers and students in universities.

In this paper, a multi-layer feed-forward neural network is used to construct a Meier spectrogram recognition system. By analyzing the algorithmic role of recurrent neural, the backpropagation algorithm is applied to update the weights in the neural network to obtain the mapping relationship between audio input and output. Combined with the algorithmic formula of the spectrum, the short-time Fourier transform is used to analyze the audio information. By architecting a multilayer feedforward recurrent neural network, the music signals are fused and classified. The cross-entropy loss function is applied to calculate the accuracy of micro and macro averages to improve the accuracy of music signal feature recognition. The results show that the feedforward recurrent neural network has the lowest error rate in different note recognition, and the error rate for “do” is 4%.

In this paper, a gray GM(1,1) model is used for modeling to realize the construction of differential equations and clustering of prediction errors into different state sets. Then the state transfer matrix is established based on the Markov chain, and the targeted prediction and correction are realized for the interval in which the relative values of the GM(1,1) model are located. For the sequence of random variables, the validity of the method is verified by using the “Marginality” test, and the prediction accuracy of the model is verified by using the residual, posterior difference and correlation tests for the gray prediction model. Finally, a Markov chain-based matching prediction method for traditional music and sports dance is proposed, the overall integration degree is measured, and its prediction matching and integration effects are analyzed. The overall integration degree of sports dance and traditional music was generally between 5.4-8, and the intensity matching coefficient of the common matching method was 100-120, while the intensity matching coefficient of the prediction method using the Markov chain model was 140-160. The actual effect of music-dance matching using the Markov chain model was good and beneficial to the integration of traditional music and sports dance.

The wide application of digital mining technology will certainly promote the development of music therapy. In this paper, we first analyze the development of music therapy and clarify the classification of orchestration. Secondly, for the time period model of mining data technology, the mathematical method of the ARMA model and the ARIMA model is investigated. Finally, the music therapy effect of guzheng performance training is predicted based on the ARIMA time series. In accordance with the playing sloped of guzheng clips from Gaojia opera, the relationship between the total number of subjects and the amount of music therapy sessions was examined, as well as the correlation among the number of active behaviors, self-expression, positive emotional expressions, and negative feelings of the subject children. The attentional concentration time showed a highly significant correlation with the number of times under music therapy, i.e., P

In this paper, firstly, the problem is modeled by a formal description of the problem of resource fragmentation in music courses for teachers, and the problem is simplified based on regional division. Secondly, an FFD-basic migration matching algorithm is proposed, and the migration matching of teaching resource nodes is realized by the least resource maximum flow model tuning. Then the measurement model of the music teaching ability of teachers was constructed, and the teaching ability measurement scale was designed. Finally, the effectiveness of the curriculum integration and reconstruction model was proved through empirical effect and analysis. The results show that the training speed is effectively increased by about 0.25 with the deep learning based model of curriculum integration and reconstruction for teachers in music. This paper provides a new solution for the integration of teaching resources in the field of music education for teachers, which has important theoretical and practical significance.

Focusing on virtual chorus, this paper analyzes the training process of VR technology and music choral conductor talents. After the construction of the online data platform, the image splicing, image alignment and image fusion algorithms of VR technology are integrated with the Harris corner detection improvement algorithm to realize the virtual chorus in the context of deep learning. The influence of family support and school aesthetic education on various dimensions of psychological needs for talent cultivation was modeled using structural equation modeling. Based on the data of music teachers using VR technology, mediated effect analysis and total effect analysis were conducted to analyze the factors influencing the use of imagery with VR technology. ANOVA was utilized to compare the data on the four dimensions of rhythmic memory, pitch, melodic memory, and listening ability to verify the effectiveness of VR technology on music teachers’ talent cultivation. The total effect analysis showed that the order of the influence of each influencing factor on the intention to use was 0.361>social influence 0.263>perceived usefulness 0.263>perceived ease of use 0.251>computer self-efficacy 0.169>convenience conditions 0.162>job relevance 0.134>simulation 0.075.

In this paper, we first use CNNS to collect rich samples of folk dance music, establish a model framework and a functional system framework, and conduct a comprehensive process analysis of the music. Then, the instrumental features, frequency features, and timbre features are extracted to obtain the spectral information. In the stage of chord analysis and encoding, a multivariate chord encoding model is established based on the acquired spectral information, including two parts: chord representation preprocessing and chord encoding. By utilizing this model, the chord structure of music was successfully and accurately encoded, allowing for analysis with up to 98% accuracy. Furthermore, significant recall results were achieved, reaching over 0.9, which suggests that the extracted chord features are highly reliable and accurate in recognizing musical chord information.

In this paper, in the process of music teaching, the music notes characterized in the time domain are Fourier transformed to the frequency domain, and the resulting notes in the frequency domain are analyzed and processed to obtain the inverted spectral domain features of the notes. On the basis of the cepstrum features, the music recognition model based on the depth confidence network is constructed, and after the training of the depth confidence network, the overfitting phenomenon often occurs for the depth confidence network, and the optimization is carried out by embedding the Dropout method between the implicit layers. From the perspective of music recognition and informationized music teaching, the research on traditional music culture integration of informationized music teaching is designed, and statistical analysis and simulation analysis methods are constructed. The results show that better recognition performance than using music features from different convolutional layers can be obtained using Deep Confidence Networks, with values of 77.5%, 78.8%, and 78.2%, respectively, and that music recognition research based on Deep Confidence Networks is able to better explore and pass on traditional music culture. In the linear regression analysis between the factors of incorporating folk songs into music teaching, the Sig. F and Sig values are 0, which are smaller than the significance level of 0.01 and 0.05, indicating that there is a significant relationship between the factors of students’ gender, age, whether they like folk songs, and the channels of exposure to folk songs and whether folk songs are incorporated into the music teaching program of colleges and universities. This study takes folk songs as representative of traditional music culture, raises people’s awareness of the value of folk songs, and enhances their understanding of the importance of music culture inheritance.

In this paper, audio and lyrics features of music are fused from the audio and lyrics attributes of modern music tracks to obtain a joint representation of music. The features such as tempo, directionality, average value of intensity, beat, and change of rhythm of each musical segment are extracted from the main audio track to form the music feature vector. Subsequently, the music was separated into multiple segments with distinct emotions, and the emotional expression of modern music was evaluated using the Hevner emotion model. Finally, the note extraction performance of this paper’s model is analyzed, and the spectral contrast features of different music pieces and the emotional expression of music pieces under different features are explored. The results show that the pitch patterns of sad emotions are mostly distributed in the range of 0.5-0.55, and the values of the pitch patterns of angry music are basically larger than 0.55. The mean values of the spectral entropy of joy and calmness are mostly distributed in the range of 0.5-0.7, and the sad emotions are mostly in the vicinity of 0.7, while the mean values of the spectral entropy of angry emotions are larger than 0.8.

Musical symbols are a good way of expressing human emotions and communication. It is a kind of nonlinear dynamic system with hysteresis. It isn’t easy to analyze its nonlinear characteristics by conventional methods. This paper divides it into several parts from the form of music. Secondly, this paper studies the local attributes of music signals and generalizes and deduces their general features. Then the Lyapunov index of music is numerically simulated, and its energy spectrum is analyzed. It is proved that the method is weak to some extent. Through the music correlation dimension analysis, we find its inherent nonlinear characteristics. The nonlinear music signals corresponding to classical piano works are given in this paper. Finally, the correlation dimension is numerically simulated by differential operation. The results show that the nonlinear characteristics of this method are stable.

In order to make spoken English more widely used in college communication, the author proposes the application of ARCS motivation model in college spoken English teaching. The author tries to apply ARCS motivation model in the strategy design of college oral English teaching, the four core factors of attention, relevance, confidence and satisfaction are explained, analyzed and explained in detail respectively, twelve practical teaching methods and strategies are summarized. A total of 109 students in 3 classes were taught for a semester, the teaching strategies and methods mentioned above were used in the weekly oral English teaching classes. After applying several teaching strategies designed by ARCS model to stimulate motivation, a post-test was conducted for all the students in the experimental class, by comparing the data before and after, the anterior and posterior sides of S1 natural consequences completely disagree: 14.67%, 13.75%, less agree: 29.35%, 24.76%, unsure: 11.04%, 1.82%, more agree: 19.26%, 33.07%, strongly agree: 25.68%, 26.6%. We can see that in terms of attention, relevance, confidence and satisfaction, the results show that ARCS motivation model is effective in college oral English teaching.

This paper builds a cloud-oriented speech teaching system based on deep learning mode. A synthetic algorithm for decision-making based on depth and hierarchy knowledge is presented. This algorithm calculates the weight matrix of the user and marks to achieve personalized speech recommendations. The whole process of cloud music teaching has been realized. The identification performance of the recorded database is compared with that of the Berlin database. It is proved that the identification ability of this algorithm is greatly improved.

"Barberpole" tempo illusions are a family of auditory illusions based on the synchronization of faded rhythmic streams playing at different rates, often manufacturing experiences of seemingly eternal acceleration or deceleration. The forefather of all such illusions, based on layers whose rates are powers of two apart ("octaves"), was studied by Jean-Claude Risset in the late seventies and is now known as Risset rhythm. This article provides a mathematical framework for barberpole tempo illusions, generalizing Risset rhythms for arbitrary numbers of subdivisions, non-integer proportions, arbitrary rate modulation, and increasingly accelerating tempi. Furthermore, this article describes a new illusion of eternal rallentando/accelerando based on the full harmonic spectrum of rates. This construction shows that Risset rhythms are related to barberpole variable-rate polyrhythms. A notable application of the study of divisional structures that barberpole illusions underpin is the construction of bistable auditory figures (accelerating or decelerating depending on the stream being focused). [ABSTRACT FROM AUTHOR]

Computer music production is an essential branch of computer technology in artistic creation. This is an interdisciplinary subject with information science and art. This paper proposes a creation model for digital music with first-order inhomogeneous first-order constant coefficient linear differential equations. This mode includes acoustic modes for waveform envelopes and bands. It has a straight-line mapping of phonetic patterns and rhythmic patterns. Then this paper uses the signal reconstruction principle and iterative extrapolation method to restore the signal in the frequency domain to obtain the simulated music. The experimental results show that this digital music creation model of inhomogeneous first-order constant coefficient linear differentiation can save a lot of labor costs. And this way can improve the audio quality and output effect. Compared with the undetermined factor method and the Laplace transform method in conventional advanced mathematics, the inhomogeneous first-order constant coefficient linear differentiation is more convenient in music composition.

The research of differential equation models in music creation and audio is a current research hotspot. This paper uses homogeneous first-order linear partial differential equations to analyze the relevant music signals in music creation. This paper focuses on analyzing the expression of music signals based on time series. This paper discusses its characteristic function and conducts partial differential equation modeling of music characteristic signals. The method successfully established the string vibration model and the time-series autoregressive model of the music signal. This paper becomes a new differential equation to solve the music signal processing method in music creation.