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Music similarity measurement and recommendation system using convolutional neural networks

Music similarity measurement and recommendation system using convolutional neural networks Due to the massive and growing volume of music on the internet, and the lack of proper management on this massive volume, similarity and music recommendation systems have been designed. The music similarity system, which is the basis of the recommendation system, can automatically generate a user's playlist according to the similar features of each piece of music. In this paper, we designed a desirable music genre classification using convolutional neural network for extracting high-level features from intermediate networks layers. For similarity measurement, we considered cosine similarity and Euclidean distances between feature vectors. We applied this automatic recommendation system on three databases with different genres and showed that the recommender achieves significant accuracy in 10-Best results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Music similarity measurement and recommendation system using convolutional neural networks

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-021-00206-5
Publisher site
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Abstract

Due to the massive and growing volume of music on the internet, and the lack of proper management on this massive volume, similarity and music recommendation systems have been designed. The music similarity system, which is the basis of the recommendation system, can automatically generate a user's playlist according to the similar features of each piece of music. In this paper, we designed a desirable music genre classification using convolutional neural network for extracting high-level features from intermediate networks layers. For similarity measurement, we considered cosine similarity and Euclidean distances between feature vectors. We applied this automatic recommendation system on three databases with different genres and showed that the recommender achieves significant accuracy in 10-Best results.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Mar 4, 2021

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