The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Polyphonic sound event detection using multi label deep neural networks
Abstract
In this paper, the use of multi label neural networks are proposed for detection of temporally overlapping sound events in realistic environments. Real-life sound recordings typically have many overlapping sound events, making it hard to recognize each event with the standard sound event detection methods. Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi label classification in this work. The model is evaluated with recordings from realistic everyday environments and the obtained overall accuracy is 63.8%. The method is compared against a state-of-the-art method using non-negative matrix factorization as a pre-processing stage and hidden Markov models as a classifier. The proposed method improves the accuracy by 19% percentage points overall.
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Cites
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Cited by
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., & Plumbley, M. D. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.3030497 · Cited by 1,235
Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338
Recurrent neural networks for polyphonic sound event detection in real life recordings
Parascandolo, G., Huttunen, H., & Virtanen, T. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2016.7472917 · Cited by 319
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Metadata
- Authors
- Cakir, E., Heittola, T., Huttunen, H., & Virtanen, T.
- Venue
- 2015 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 1-7
- OpenAlex
- W1650531274
- Cited by
- 256
- References
- 1
- FWCI
- 24.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cakir, E., Heittola, T., Huttunen, H., & Virtanen, T. (2015). Polyphonic sound event detection using multi label deep neural networks. 2015 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2015.7280624
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