Name
Abstract | ||
|---|---|---|
Acoustic scenes (or soundscapes) can be composed of various background and foreground sound events. Classification of soundscapes have to deal with the overlap of these sound events. In this work, we propose to reduce this overlap by considering foreground sound events and background sound events as multiple views of the soundscape. Robust principal components analysis is used to decompose a soundscape into the background view and the foreground view. We can control the amount of information retained in this decomposition by using the subspace projection technique of nuisance attribute projection. We represent the audio samples as features from a convolutional neural network, and view-invariant representations are derived using a deep neural network based multi-view learning algorithm. Experimental results demonstrate the effectiveness of our proposed method on standard datasets for acoustic scene classification. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TASLP.2022.3153272 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | DocType | Volume |
Acoustic scene classification,multi-view learning,robust principal component analysis,subspace projection | Journal | 30 |
Issue | ISSN | Citations |
1 | 2329-9290 | 0 |
PageRank | References | Authors |
0.34 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dhanunjaya Varma Devalraju | 1 | 0 | 0.34 |
| Padmanabhan Rajan | 2 | 22 | 7.63 |