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Exploiting Heterogeneous Artist and Listener Preference Graph for Music Genre Classification

Published: 12 October 2020 Publication History

Abstract

Music genres are useful for indexing, organizing, searching, and recommending songs and albums. Therefore, the automatic classification of music genres is an essential part of almost all kinds of music applications. Recent works focus on exploiting text, audio, or multi-modal information for genre classification, without considering the influence of the artists' and listeners' preference. However, intuitively, artists have their composing preferences, and listeners also have their music tastes. Both of them provide helpful hints to the music genre from different views, which are crucial to improve classification performance.
In this paper, we make use of both artist-music and listener-music preference relations to construct a heterogeneous preference graph. Then, we propose a novel graph-based neural network to automatically encode the global preference relations of the heterogeneous graph into artist and listener representations. We construct a graph to capture the correlations among genres and apply a graph convolutional network to learn genre representation from the correlation graph. Finally, we combine artist, listener, and genre representations for multi-label genre classification. Experimental results show that our model significantly outperforms the state-of-the-art methods on two public music genre classification datasets.

Supplementary Material

MP4 File (3394171.3414000.mp4)
This video is a presentation of the paper "Exploiting Heterogeneous Artist and Listener Preference Graph for Music Genre Classification". In this video, we introduce the multi-label music genre classification task and our solution to this task. The duration of the video is 4 minutes and 38 seconds.

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cover image ACM Conferences
MM '20: Proceedings of the 28th ACM International Conference on Multimedia
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 12 October 2020

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Author Tags

  1. graph neural network
  2. label correlations
  3. multi-label classification
  4. music genre classification
  5. preference graph

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  • Research-article

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  • the Beijing Municipal Science and Technology Project

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2024)Dual-verification-based model fingerprints against ambiguity attacksCybersecurity10.1186/s42400-024-00298-67:1Online publication date: 23-Dec-2024
  • (2024)Automated Music Classification using Machine Learning for Indian Songs2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)10.1109/NMITCON62075.2024.10698871(1-6)Online publication date: 9-Aug-2024
  • (2023)Novel mathematical model for the classification of music and rhythmic genre using deep neural networkJournal of Big Data10.1186/s40537-023-00789-210:1Online publication date: 21-Jun-2023
  • (2022)Music genre classification based on fusing audio and lyric informationMultimedia Tools and Applications10.1007/s11042-022-14252-682:13(20157-20176)Online publication date: 29-Dec-2022

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