Sound event detection via dilated convolutional recurrent neural networks
| Autor: | Tuomas Virtanen, Yanxiong Li, Konstantinos Drossos, Mingle Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Word error rate 020206 networking & telecommunications Pattern recognition 02 engineering and technology Sound event detection 030507 speech-language pathology & audiology 03 medical and health sciences Recurrent neural network Kernel (image processing) Receptive field Audio and Speech Processing (eess.AS) 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Artificial intelligence 0305 other medical science F1 score business Classifier (UML) Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ICASSP |
| Popis: | Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely a CRNN with a dilated convolutional kernel, as the classifier for the task of SED. We investigate the effectiveness of dilation operations which provide a CRNN with expanded receptive fields to capture long temporal context without increasing the amount of CRNN's parameters. Compared to the classifier of the baseline CRNN, the classifier of the dilated CRNN obtains a maximum increase of 1.9%, 6.3% and 2.5% at F1 score and a maximum decrease of 1.7%, 4.1% and 3.9% at error rate (ER), on the publicly available audio corpora of the TUT-SED Synthetic 2016, the TUT Sound Event 2016 and the TUT Sound Event 2017, respectively. 5 pages, 3 tables and 3 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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