A Study on Broadcast Networks for Music Genre Classification

Ahmed Heakl, Abdelrahman Abdelgawad, Victor Parque

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728186719
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 International Joint Conference on Neural Networks, IJCNN 2022 - Padua, Italy
Duration: 2022 Jul 182022 Jul 23

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2022-July

Conference

Conference2022 International Joint Conference on Neural Networks, IJCNN 2022
Country/TerritoryItaly
CityPadua
Period22/7/1822/7/23

Keywords

  • broadcast networks
  • convolutional neural networks
  • music classification
  • music genre classification

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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