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Tampere University of Technology Sound event detection using spatial features and convolutional recurrent neural network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pertilä, Pasi Virtanen, Tuomas |
| Copyright Year | 2017 |
| Abstract | This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of them separately in the initial stages. We show that instead of concatenating the features of each channel into a single feature vector the network learns sound events in multichannel audio better when they are presented as separate layers of a volume. Using the proposed spatial features over monaural features on the same network gives an absolute F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and 2.7% on the TUT-SED 2009 dataset that is fifteen times larger. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://tutcris.tut.fi/portal/files/9459100/icassp_2017_paper.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
