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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 extrac...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970704/ https://www.ncbi.nlm.nih.gov/pubmed/36860420 http://dx.doi.org/10.1155/2023/1263620 |
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author | Wang, Xiuli |
author_facet | Wang, Xiuli |
author_sort | Wang, Xiuli |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9970704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-99707042023-02-28 Music Similarity Detection Guided by Deep Learning Model Wang, Xiuli Comput Intell Neurosci Research Article 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. Hindawi 2023-02-20 /pmc/articles/PMC9970704/ /pubmed/36860420 http://dx.doi.org/10.1155/2023/1263620 Text en Copyright © 2023 Xiuli Wang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Xiuli Music Similarity Detection Guided by Deep Learning Model |
title | Music Similarity Detection Guided by Deep Learning Model |
title_full | Music Similarity Detection Guided by Deep Learning Model |
title_fullStr | Music Similarity Detection Guided by Deep Learning Model |
title_full_unstemmed | Music Similarity Detection Guided by Deep Learning Model |
title_short | Music Similarity Detection Guided by Deep Learning Model |
title_sort | music similarity detection guided by deep learning model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970704/ https://www.ncbi.nlm.nih.gov/pubmed/36860420 http://dx.doi.org/10.1155/2023/1263620 |
work_keys_str_mv | AT wangxiuli musicsimilaritydetectionguidedbydeeplearningmodel |