Music genre classification and music recommendation by using deep learning

Elbir, Ahmet; Aydın, Nizamettin

doi:10.1049/el.2019.4202

Cited by 82 publications

(29 citation statements)

References 4 publications

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“…Ruhalahti et al constructed the teacher's training assistance method using DL and inspired the thinking on the new development stage of designing and creating collaborative, self-management, and dialogue knowledge process [9]. Elbir et al established a music scoring and recommendation model based on NN technology, which described music and score music with vectors [10]. Liu et al proposed a combined algorithm for dam deformation prediction based on two traditional models and an optimized model, which combined two subalgorithms: GM (Grey Model) (1, 1) and BPNN [11].…”

Section: Related Workmentioning

confidence: 99%

Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology

Liu

2022

Computational Intelligence and Neuroscience

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At present, there are many chess styles in piano education, but there is a lack of comprehensive, scientific, and guiding teaching mode. It highlights many educational problems and cannot meet the development requirements of piano education at this stage. However, the piano scoring system can partially replace teachers’ guidance to piano players. This paper extracts the signal characteristics of playing music, establishes the piano performance scoring model using Big Data and BP neural network technology, and selects famous works to test the effect of the scoring system. The results show that the model can test whether the piano works fairly. It can effectively evaluate the player’s performance level and accurately score each piece of music. This not only provides a reference for the player to improve the music level but also provides a new idea for the research results and the application of new technology in music teaching. This paper puts forward reasonable solutions to the problems existing in piano education at the present stage, which is helpful to cultivate high-quality piano talents. Experiments show that the application of Big Data technology and BP neural network to optimize the piano performance scoring system is effective and can score piano music accurately. This paper studies the performance scoring system and gets the model after training, which can replace music teachers and alleviate the shortage of music teachers in the market.

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Section: Related Workmentioning

confidence: 99%

Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology

Liu

2022

Computational Intelligence and Neuroscience

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“…It can also promote the development of music literacy. Elbir et al [16] mainly study the music recommendation system of the music monitoring platform, which can complete the classification of music forms and genres according to people's preferences. It mainly uses deep learning neural network models to extract musical features based on acoustic properties.…”

Section: Related Workmentioning

confidence: 99%

Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation

Zhang

Gong

2022

Discrete Dynamics in Nature and Society

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Music art is a form of conveying cultural information and humanistic emotions. From ancient times to the present, the form of music has undergone great changes. The change of music form is closely related to the change in dynasties, the development of the market economy, and the change in humanistic spirit. Today, the development of music has reached a relatively prosperous stage, which is closely related to the rapid development of the market economy. At the heart of the quantitative analysis is the study of associations between data. However, it will be a difficult task to analyze the relationship between the development scale of the market economy and the form of music innovation only by artificial means. This research mainly uses a convolutional neural network and long and short-term memory neural network technology to quantitatively analyze the relationship between the development scale of the market economy and the form of music innovation, which is mainly a quantitative analysis of classical music, pop music, and rap music. The research results show that convolutional neural networks and long short-term memory neural networks have sufficient capabilities to quantitatively analyze the relationship between the market development scale and the form of music innovation. Neural networks with LSTM layers have lower errors in predicting market economic correlates than neural network methods without LSTM layers. The error is reduced by 0.27%. For the prediction of innovative forms of music, the largest prediction error is only 2.93%, which is closely related to the variability of popular music. The linear correlation indices for the predictions of the three forms of musical innovation also all exceed 0.955.

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“…At present, there is no parameter in feature extraction that can represent all useful information of music signals, even if the more mature MFCC parameters are used [10]. Among the various parameters, it is an approximate description of a certain aspect of the music signal.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Music Signal Based on Adaptive Wave Equation Inversion

Gong

Sun

2021

Advances in Mathematical Physics

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The digitization, analysis, and processing technology of music signals are the core of digital music technology. There is generally a preprocessing process before the music signal processing. The preprocessing process usually includes antialiasing filtering, digitization, preemphasis, windowing, and framing. Songs in the popular wav format and MP3 format on the Internet are all songs that have been processed by digital technology and do not need to be digitalized. Preprocessing can affect the effectiveness and reliability of the feature parameter extraction of music signals. Since the music signal is a kind of voice signal, the processing of the voice is also applicable to the music signal. In the study of adaptive wave equation inversion, the traditional full-wave equation inversion uses the minimum mean square error between real data and simulated data as the objective function. The gradient direction is determined by the cross-correlation of the back propagation residual wave field and the forward simulation wave field with respect to the second derivative of time. When there is a big gap between the initial model and the formal model, the phenomenon of cycle jumping will inevitably appear. In this paper, adaptive wave equation inversion is used. This method adopts the idea of penalty function and introduces the Wiener filter to establish a dual objective function for the phase difference that appears in the inversion. This article discusses the calculation formulas of the accompanying source, gradient, and iteration step length and uses the conjugate gradient method to iteratively reduce the phase difference. In the test function group and the recorded music signal library, a large number of simulation experiments and comparative analysis of the music signal recognition experiment were performed on the extracted features, which verified the time-frequency analysis performance of the wave equation inversion and the improvement of the decomposition algorithm. The features extracted by the wave equation inversion have a higher recognition rate than the features extracted based on the standard decomposition algorithm, which verifies that the wave equation inversion has a better decomposition ability.

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Music genre classification and music recommendation by using deep learning

Cited by 82 publications

References 4 publications

Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology

Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology

Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation

Feature Extraction of Music Signal Based on Adaptive Wave Equation Inversion

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