Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18) |
| Pages | 65-71 |
| Number of pages | 7 |
| Publication status | Published - 4 Sep 2018 |
| Event | 21st International Conference on Digital Audio Effects - Universidade de Aveiro, Aveiro, Portugal Duration: 4 Sep 2018 → 8 Sep 2018 http://dafx2018.web.ua.pt/ (Link to Conference Website) |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 2413-6700 |
| ISSN (Electronic) | 2413-6689 |
Conference
| Conference | 21st International Conference on Digital Audio Effects |
|---|---|
| Abbreviated title | DAFx-18 |
| Country | Portugal |
| City | Aveiro |
| Period | 4/09/18 → 8/09/18 |
| Internet address |
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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 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)
ER -