HDAWN-HRDAE: A hybrid framework for the music genre classification with the dual temporal and frequency feature modeling

Xiaoxu Jie; Mohammad Khishe; F. Hernando-Galego; Diego Martín; Rifaqat Ali; Qusai Y. Shambour; Taher M. Ghazal

doi:10.1016/j.asej.2026.104115

Publication record · 2026

HDAWN-HRDAE: A hybrid framework for the music genre classification with the dual temporal and frequency feature modeling

Authors: Xiaoxu Jie · Mohammad Khishe · F. Hernando-Galego · Diego Martín · Rifaqat Ali · Qusai Y. Shambour · Taher M. Ghazal
Year: 2026
Publisher: Elsevier
Journal or publication: Ain Shams Engineering Journal
Google Scholar citations: 0

Journal indicators

CiteScore 2025 (Scopus)12.4

Best quartileQ1

Top 10% in any categoryYes

Positions by category

Engineering (all)16/351 · Q1 · percentile 95 · Top 10%

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Not identified in the supplied report on non-standard bibliometric behaviour (2017–2019).

The report is an independent analysis published in 2021; it is not an official ANECA ban or endorsement list.

Research summary

HDAWN-HRDAE combines deep autoencoders, wavelet networks and recurrent temporal modelling for music-genre recognition. On GTZAN it targets accurate classification with substantially lower computational complexity than deeper convolutional alternatives.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.1016/j.asej.2026.104115 ↗ · Open-access version ↗ · View Google Scholar record ↗