Music Genre Classification Using Deep Learning

Mounika, Karakavalasa; Deyaradevi, S; Swetha, K.; Vanitha, V.

doi:10.1109/icaeca52838.2021.9675685

Cited by 8 publications

(11 citation statements)

References 6 publications

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“…In formula (8), M (l) ij is used to describe the weight coefficient between the i and j nodes in the l and l + 1 layers, v (l) i is used to describe the i node offset in the l layer, and h R,v (s (i) ) is used to describe the result after s (i) reconstruction. e difference between the original input and the current input after reconstruction is calculated, that is, the mean square error term.…”

Section: Deep Belief Networkmentioning

confidence: 99%

“…e classification algorithm is based on the combination of a deep belief network and sparse representation. e methods of reference [7] and the methods of reference [8] are compared with the proposed methods to verify the effectiveness of the proposed methods.…”

Section: Experimental Environment and Data Setmentioning

confidence: 99%

“…In order to verify the classification accuracy of the proposed method, 1000 music genre samples are selected, and the methods of reference [7] and the methods of reference [8] and the proposed method are used for automatic classification of music genres, respectively. According to formula (29), the accuracy of automatic classification of music genres by different methods is calculated, and the comparison results are shown in Figure 7.…”

Section: Accuracy Of Automatic Classification Of Music Genresmentioning

confidence: 99%

“…According to formula (29), the accuracy of automatic classification of music genres by different methods is calculated, and the comparison results are shown in Figure 7. It can be seen from Figure 7 that under the number of 1000 music genre samples, the average accuracy of automatic music genre classification of the methods of reference [7] is 88%, the average accuracy of automatic music genre classification of the methods of reference [8] method is 82%, and the average accuracy of automatic music genre classification of the proposed method is as high as 95%. It can be seen that compared with the methods of reference [7] and the methods of reference [8], the proposed method has higher accuracy in automatic classification of music genres and can effectively improve the accuracy of automatic classification of music genres.…”

Section: Accuracy Of Automatic Classification Of Music Genresmentioning

confidence: 99%

“…is method is effective. Reference [8] proposed a music genre classification method based on deep learning. Machine learning technology is used to classify music types.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

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Section: Deep Belief Networkmentioning

confidence: 99%

Section: Experimental Environment and Data Setmentioning

confidence: 99%

Section: Accuracy Of Automatic Classification Of Music Genresmentioning

confidence: 99%

Section: Accuracy Of Automatic Classification Of Music Genresmentioning

confidence: 99%

“…is method is effective. Reference [8] proposed a music genre classification method based on deep learning. Machine learning technology is used to classify music types.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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Multilingual Indian Musical Type Classification

Aswale

Shrivastava²,

Bhagat³

et al. 2023

Lecture Notes in Electrical Engineering

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A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Music Genre Classification Using Deep Learning

Cited by 8 publications

References 6 publications

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Multilingual Indian Musical Type Classification

A Survey on Artificial Intelligence-Based Acoustic Source Identification

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