A Study on Broadcast Networks for Music Genre Classification

Ahmed Heakl, Abdelrahman Abdelgawad, Victor Parque

研究成果: Conference contribution

抄録

Due to the increased demand for music streaming/recommender services and the recent developments of music information retrieval frameworks, Music Genre Classification (MGC) has attracted the community's attention. However, convolutional-based approaches are known to lack the ability to efficiently encode and localize temporal features. In this paper, we study the broadcast-based neural networks aiming to improve the localization and generalizability under a small set of parameters (about 180k) and investigate twelve variants of broadcast networks discussing the effect of block configuration, pooling method, activation function, normalization mechanism, label smoothing, channel interdependency, LSTM block inclusion, and variants of inception schemes. Our computational experiments using relevant datasets such as GTZAN, Extended Ballroom, HOMBURG, and Free Music Archive (FMA) show the state-of-the-art classification accuracies in MGC. Our approach offers insights and the potential to enable compact and generalizable broadcast networks for music classification.

本文言語English
ホスト出版物のタイトル2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728186719
DOI
出版ステータスPublished - 2022
外部発表はい
イベント2022 International Joint Conference on Neural Networks, IJCNN 2022 - Padua, Italy
継続期間: 2022 7月 182022 7月 23

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2022-July

Conference

Conference2022 International Joint Conference on Neural Networks, IJCNN 2022
国/地域Italy
CityPadua
Period22/7/1822/7/23

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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