The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Open accessAbstract
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNNs) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a convolutional recurrent neural network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.
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Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
Mesaros, A., Heittola, T., Benetos, E., Foster, P., Lagrange, M., Virtanen, T., & Plumbley, M. D. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2778423 · Cited by 298
Self-citationComparison of Pre-Trained CNNs for Audio Classification Using Transfer Learning
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Metadata
- Authors
- Cakir, E., Parascandolo, G., Heittola, T., Huttunen, H., & Virtanen, T.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2017-06
- Volume
- 25
- Issue
- 6
- Pages
- 1291-1303
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W2591013610
- Cited by
- 604
- References
- 15
- FWCI
- 53.94
- Type
- journal article
- Language
- en
- Open access
- green
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- Cakir, E., Parascandolo, G., Heittola, T., Huttunen, H., & Virtanen, T. (2017). Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing. https://doi.org/10.1109/taslp.2017.2690575
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