The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Recurrent neural networks for polyphonic sound event detection in real life recordings
Open accessAbstract
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs). A single multilabel BLSTM RNN is trained to map acoustic features of a mixture signal consisting of sounds from multiple classes, to binary activity indicators of each event class. Our method is tested on a large database of real-life recordings, with 61 classes (e.g. music, car, speech) from 10 different everyday contexts. The proposed method outperforms previous approaches by a large margin, and the results are further improved using data augmentation techniques. Overall, our system reports an average F1-score of 65.5% on 1 second blocks and 64.7% on single frames, a relative improvement over previous state-of-the-art approach of 6.8% and 15.1% respectively.
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Polyphonic sound event detection using multi label deep neural networks
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Self-citationAcoustic event detection in real life recordings
Mesaros, A., Heittola, T., Eronen, A., et al. (2010) · Cited by 221
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Cohen, Y., Nicholson, D. A., Sanchioni, A., Mallaber, E. K., Skidanova, V., & Gardner, T. J. (2022) · eLife · doi:10.7554/elife.63853 · Cited by 80
VIMES: A Wearable Memory Assistance System for Automatic Information Retrieval
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Metadata
- Authors
- Parascandolo, G., Huttunen, H., & Virtanen, T.
- Venue
- 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 6440-6444
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2341412280
- Cited by
- 319
- References
- 19
- FWCI
- 39.16
- Type
- conference paper
- Language
- en
- Open access
- green
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- Parascandolo, G., Huttunen, H., & Virtanen, T. (2016). Recurrent neural networks for polyphonic sound event detection in real life recordings. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2016.7472917
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