Music Style Classification Research Articles

Constructing a Theoretical Methodological System for Vocal Music Education in Colleges and Universities in the Context of Deep Learning Algorithms

Abstract In recent years, artificial intelligence technology has been deeply integrated into college education to comply with the call for teaching reform. The research first uses endpoint detection and Mel frequency cepstrum coefficients to realize the recognition and extraction of vocal music feature signals. Then it uses a deep convolutional neural network to output the extracted signals to complete the recognition and error correction of different vocal music. Finally, different vocal audio is used as a sample to deeply analyze the recognition of various styles of vocal music, as well as the recognition and error correction of music. The model in this paper has a more satisfactory effect on vocal music recognition and music style classification. The recognition rate of vocal music under different signal-to-noise ratios is above 90%. It can also well realize the error correction of vocal music and accurately judge the time value (95%) and pitch (93.57%) of the playing, which reflects a high application value in vocal music teaching. The use of deep learning in vocal music recognition and error correction in college vocal music education can assist vocal music learners and decrease the work intensity of vocal music educators.

Music Similarity Detection Guided by Deep Learning Model.

Digital music has become a hot spot with the rapid development of network technology and digital audio technology. The general public is increasingly interested in music similarity detection (MSD). Similarity detection is mainly for music style classification. The core MSD process is to first extract music features, then implement training modeling, and finally input music features into the model for detection. Deep learning (DL) is a relatively new feature extraction technology to improve the extraction efficiency of music features. This paper first introduces the convolutional neural network (CNN) of DL algorithms and MSD. Then, an MSD algorithm is constructed based on CNN. Besides, the Harmony and Percussive Source Separation (HPSS) algorithm separates the original music signal spectrogram and decomposes it into two components: time characteristic harmonics and frequency characteristic shocks. These two elements are input to the CNN together with the data in the original spectrogram for processing. In addition, the training-related hyperparameters are adjusted, and the dataset is expanded to explore the influence of different parameters in the network structure on the music detection rate. Experiments on the GTZAN Genre Collection music dataset show that this method can effectively improve MSD using a single feature. The final detection result is 75.6%, indicating the superiority of this method compared with other classical detection methods.

Retracted: Research on Music Style Classification Based on Deep Learning.

[This retracts the article DOI: 10.1155/2022/3699885.].

Research on Music Style Classification and health care Based on Neural Network

The current music style classification method is based on high-dimensional feature matrix, which has the problem of large space cost and low classification accuracy. In view of the above problems, this paper studies the music style classification method based on neural network. The MFCC features of music are extracted by processing the music to be classified in two steps: weighting and windowing. The RNN neural network is trained by the sample music set to classify the music styles. Simulation results show that compared with the traditional method, the proposed music style classification method improves the classification accuracy by at least 16.36%, and the space and time cost of the method is small, and the practical application effect is better.

Research on Music Style Classification Based on Deep Learning.

Music style is one of the important labels for music classification, and the current music style classification methods extract features such as rhythm and timbre of music and use classifiers to achieve classification. The classification accuracy is not only affected by the classifier but also limited by the effect of music feature extraction, which leads to poor classification accuracy and stability. In response to the abovementioned defects, a deep-learning-based music style classification method will be studied. The music signal is framed using filters and Hamming windows, and the MFCC coefficient features of music are extracted by discrete Fourier transform. A convolutional recurrent neural network structure combining CNN and RNN is designed and trained to determine the parameters to achieve music style classification. Analysis of the simulation experimental data shows that the classification accuracy of the studied classification method is at least 93.3%, and the classification time overhead is significantly reduced, the classification results are stable, and the results are reliable.

Practice and Exploration of Music Solfeggio Teaching Based on Data Mining Technology.

People can learn, understand, and use music using the solfeggio method. People involved in professional music training have given it importance because it is a fundamental discipline of enlightenment music and a necessary training method and technical theory course to enter the professional level. This article presents an in-depth analysis of the music curriculum model based on data mining technology. Additionally, a music style classification method based on FP Growth association rules mining is developed, which decreases the number of frequent element sets needed and speeds up database scanning. To better match the characteristics of music, a multirepetition structure model is used, and the MFCC (Mel-frequency cepstral coefficients) function is used to set the repetition structure model. The results demonstrate that the music style classification method put forth in this article has clear advantages in terms of efficiency: when the minimum support is 0.2 percent, this method's execution time is about 33 percent compared to the other methods. The outcomes of the experiment suggest that this method can more accurately capture the repeatability of music and enhance separation performance to some extent.

Transformation of Nonmultiple Cluster Music Cyclic Shift Topology to Music Performance Style

Music is an abstract art form that uses sound as its means of expression. It has deeply affected our lives. This paper proposes a method for extracting segment features from nonmultiple cluster music files. We divide each piece of music into multiple segments and extract the features of each segment. The specific process includes nonmultiple cluster music file note extraction, main melody extraction, segment division, and segment feature extraction. The segment feature is extracted from a segment of a piece of music, contains the main melody and accompaniment information of the segment, and can reflect the sequence relationship of the notes. This paper proposes a performance style conversion network based on recurrent neural network and convolutional neural network. The bidirectional recurrent neural network based on Gated Recurrent Unit (GRU) is used to extract different styles of note feature vector sequences, and the extracted note feature vector sequence is used to predict the intensity of a specific style, and the intensity changes of different styles of nonmultiple cluster music are better learned. Through the comparison, the multiclassification strategy of “one-to-the-rest” is selected, and the fuzzy recurrent neural network is applied to the shortcomings of the unrecognizable area. Finally, according to the feature extraction method and the principle of the classifier algorithm studied in this paper, a music style classification system is implemented in the MATLAB environment. Experimental simulation shows that this system can effectively classify music performance styles.

Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network

The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of music‐style classification. This paper proposes a novel method based on music extraction and deep neural networks to address the problem of low accuracy in traditional methods. The music style classification algorithm extracts two types of features as classification characteristics for music styles: timbre and melody features. Because the classification method based on a convolutional neural network ignores the audio’s timing. As a result, we proposed a music classification module based on the one‐dimensional convolution of a recurring neuronal network, which we combined with single‐dimensional convolution and a two‐way, recurrent neural network. To better represent the music style properties, different weights are applied to the output. The GTZAN data set was also subjected to comparison and ablation experiments. The test results outperformed a number of other well‐known methods, and the rating performance was competitive.

Data Mining Applied to Music Style Classification

Data Mining Applied to Music Style Classification

The evolution of popular music: USA 1960-2010.

In modern societies, cultural change seems ceaseless. The flux of fashion is especially obvious for popular music. While much has been written about the origin and evolution of pop, most claims about its history are anecdotal rather than scientific in nature. To rectify this, we investigate the US Billboard Hot 100 between 1960 and 2010. Using music information retrieval and text-mining tools, we analyse the musical properties of approximately 17 000 recordings that appeared in the charts and demonstrate quantitative trends in their harmonic and timbral properties. We then use these properties to produce an audio-based classification of musical styles and study the evolution of musical diversity and disparity, testing, and rejecting, several classical theories of cultural change. Finally, we investigate whether pop musical evolution has been gradual or punctuated. We show that, although pop music has evolved continuously, it did so with particular rapidity during three stylistic ‘revolutions’ around 1964, 1983 and 1991. We conclude by discussing how our study points the way to a quantitative science of cultural change.

Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons.

The ability to process complex spatiotemporal information is a fundamental process underlying the behavior of all higher organisms. However, how the brain processes information in the temporal domain remains incompletely understood. We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture. By combining optogenetics with microelectrode array recording, we show that these randomly organized cortical microcircuits are able to process complex spatiotemporal information, allowing the identification of a large number of temporal sequences and classification of musical styles. These experiments uncovered spatiotemporal memory processes lasting several seconds. Neural network simulations indicated that both short-term synaptic plasticity and recurrent connections are required for the emergence of this capability. Interestingly, NMDA receptor function is not a requisite for these short-term spatiotemporal memory processes. Indeed, blocking the NMDA receptor with the antagonist APV significantly improved the temporal processing ability of the networks, by reducing spontaneously occurring network bursts. These highly synchronized events have disastrous effects on spatiotemporal information processing, by transiently erasing short-term memory. These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits.

The evolution of music—A comparison of Darwinian and dialectical methods

Music provides a challenging system for the analysis of cultural evolution. The dialectical approach to music seeks to identify the internal stylistic tensions and contradictions (in terms of thesis and antithesis) which give rise to new musical forms (synthesis). The Darwinian alternative to dialectics, which in its most reductionist form is becoming known as memetics, seeks to interpret the evolution of music by examining the adaptiveness of its various component parts in the selective environment of culture. This essay compares the memetic and dialectical approaches with special reference to the development of jazz in the era of recorded sound, in the light of Benzon's classification of musical styles into evolutionary Ranks (Benzon, 1993). This essay concludes that the basic postulate of memetics is falsifiable and therefore that memetics qualifies as scientific in the Popperian sense, rather than being simply a pseudo-scientific meta-narrative for cultural evolution. Some suggestions for empirical analysis are provided. In contrast, the dialectical perspective is not scientific in the Popperian sense, but does provide a good explanatory framework for the history of jazz in the years 1900–1970, and shows how transitions between “ranks” (from Benzon) may be generated. However, dialectics is considerably less successful in the construction of a model to explain the period since 1970.