Drum translation for timbral and rhythmic transformation

Tomczak, Maciej and Drysdale, Jake and Hockman, Jason (2019) Drum translation for timbral and rhythmic transformation. In: Digital Audio Effects 2019, 3rd - 6th September 2019, Birmingham City University.

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Abstract

Many recent approaches to creative transformations of musical audio have been motivated by the success of raw audio generation models such as WaveNet, in which audio samples are modeled by generative neural networks. This paper describes a generative audio synthesis model for multi-drum translation based on a WaveNet denosing autoencoder architecture. The timbre of an arbitrary source audio input is transformed to sound as if it were played by various percussive instruments while preserving its rhythmic structure. Two evaluations of the transformations are conducted based on the capacity of the model to preserve the rhythmic patterns of the input and the audio quality as it relates to timbre of the target drum domain. The first evaluation measures the rhythmic similarities between the source audio and the corresponding drum translations, and the second provides a numerical analysis of the quality of the synthesised audio. Additionally, a semi- and fully-automatic audio effect has been proposed, in which the user may assist the system by manually labelling source audio segments or use a state-of-the-art automatic drum transcription system prior to drum translation.

Item Type:	Conference or Workshop Item (Paper)
Dates:	Date Event 1 June 2019 Accepted 6 September 2019 Published Online
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-04 - software engineering CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence CAH25 - design, and creative and performing arts > CAH25-02 - performing arts > CAH25-02-02 - music
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Jason Hockman
Date Deposited:	01 Apr 2022 11:00
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/13022

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