Deep Neural Networks with Music Dereverberation for Technical Ear Training in Music Production Education

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Number of pages21
Journal / PublicationAsia-Pacific Journal for Arts Education
Issue numberSpecial Issue 2023
Publication statusPublished - 2023

Conference

TitleInternational Conference on Music Education Technology 2023 (ICMdT2023)
LocationHybrid, Education University of Hong Kong
PlaceHong Kong
Period10 - 12 January 2023

Abstract

Reverberation is an audio effect used in music production affecting the audio spectrum, as well as the timbre, of music. Technical ear training concerning the ways that reverberation works on music samples is important, since in order to achieve the desired texture sound engineers need to be able to perceive subtle audio changes. However, “dry” recordings, i.e. those that lack reverberation, are not universally available for music production students for the purposes of practice. In this paper, we propose a deep learning-based music dereverberation method to generate de-reverbed music samples for technical ear training in music production education. The experiment results show that based on various objective evaluation metrics, the proposed method can effectively realize dereverberation compared to other neural network-based methods.

Research Area(s)

  • (de)reverberation, audio spectrum, timbre, texture, technical ear training, music production