Stationary/transient audio separation using convolutional autoencoders

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

Abstract

Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content pro- duction. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsuper- vised algorithms for individual and and joint separation. We de- scribe our implementation and show examples. Our results show promise for the use of convolutional autoencoders in the extraction of sparse components from audio spectrograms, particularly using monophonic sounds.
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Digital Audio Effects (DAFx-18)
Pages65-71
Number of pages7
Publication statusPublished - 4 Sep 2018
Event21st International Conference on Digital Audio Effects - Universidade de Aveiro, Aveiro, Portugal
Duration: 4 Sep 20188 Sep 2018
http://dafx2018.web.ua.pt/ (Link to Conference Website)

Publication series

Name
ISSN (Print)2413-6700
ISSN (Electronic)2413-6689

Conference

Conference21st International Conference on Digital Audio Effects
Abbreviated titleDAFx-18
CountryPortugal
CityAveiro
Period4/09/188/09/18
Internet address

Fingerprint

Acoustic waves

Cite this

Roma, G., Green, O., & Tremblay, P. A. (2018). Stationary/transient audio separation using convolutional autoencoders. In Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18) (pp. 65-71)
Roma, Gerard ; Green, Owen ; Tremblay, Pierre Alexandre. / Stationary/transient audio separation using convolutional autoencoders. Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18). 2018. pp. 65-71
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title = "Stationary/transient audio separation using convolutional autoencoders",
abstract = "Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content pro- duction. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsuper- vised algorithms for individual and and joint separation. We de- scribe our implementation and show examples. Our results show promise for the use of convolutional autoencoders in the extraction of sparse components from audio spectrograms, particularly using monophonic sounds.",
author = "Gerard Roma and Owen Green and Tremblay, {Pierre Alexandre}",
year = "2018",
month = "9",
day = "4",
language = "English",
pages = "65--71",
booktitle = "Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18)",

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Roma, G, Green, O & Tremblay, PA 2018, Stationary/transient audio separation using convolutional autoencoders. in Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18). pp. 65-71, 21st International Conference on Digital Audio Effects, Aveiro, Portugal, 4/09/18.

Stationary/transient audio separation using convolutional autoencoders. / Roma, Gerard; Green, Owen; Tremblay, Pierre Alexandre.

Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18). 2018. p. 65-71.

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

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T1 - Stationary/transient audio separation using convolutional autoencoders

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AU - Green, Owen

AU - Tremblay, Pierre Alexandre

PY - 2018/9/4

Y1 - 2018/9/4

N2 - Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content pro- duction. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsuper- vised algorithms for individual and and joint separation. We de- scribe our implementation and show examples. Our results show promise for the use of convolutional autoencoders in the extraction of sparse components from audio spectrograms, particularly using monophonic sounds.

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M3 - Conference contribution

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EP - 71

BT - Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18)

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Roma G, Green O, Tremblay PA. Stationary/transient audio separation using convolutional autoencoders. In Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18). 2018. p. 65-71