Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language
English
Title of host publication
Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18)
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)",
Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18). 2018. p. 65-71.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Stationary/transient audio separation using convolutional autoencoders
AU - Roma, Gerard
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.
AB - 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.
M3 - Conference contribution
SP - 65
EP - 71
BT - Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18)