The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Very deep convolutional neural networks for raw waveforms
Open accessAbstract
Learning acoustic models directly from the raw waveform data with minimal processing is challenging. Current waveform-based models have generally used very few (~2) convolutional layers, which might be insufficient for building high-level discriminative features. In this work, we propose very deep convolutional neural networks (CNNs) that directly use time-domain waveforms as inputs. Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e.g., vector of size 32000), necessary for processing acoustic waveforms. This is achieved through batch normalization, residual learning, and a careful design of down-sampling in the initial layers. Our networks are fully convolutional, without the use of fully connected layers and dropout, to maximize representation learning. We use a large receptive field in the first convolutional layer to mimic bandpass filters, but very small receptive fields subsequently to control the model capacity. We demonstrate the performance gains with the deeper models. Our evaluation shows that the CNN with 18 weight layers outperforms the CNN with 3 weight layers by over 15% in absolute accuracy for an environmental sound recognition task and is competitive with the performance of models using log-mel features.
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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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
Realistic Speech-Driven Facial Animation with GANs
Vougioukas, K., Petridis, S., & Pantic, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01251-8 · Cited by 298
Dilated convolution neural network with LeakyReLU for environmental sound classification
Zhang, X., Zou, Y., & Shi, W. (2017) · 2017 22nd International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2017.8096153 · Cited by 283
End-to-end convolutional neural network model for gear fault diagnosis based on sound signals
Yao, Y., Wang, H., Li, S., Liu, Z., Gui, G., Dan, Y., & Hu, J. (2018) · Applied Sciences · doi:10.3390/app8091584 · Cited by 120
A convolutional neural network to filter artifacts in spectroscopic MRI
Gurbani, S. S., Schreibmann, E., Maudsley, A. A., Cordova, J. S., Soher, B. J., Poptani, H., Verma, G., Barker, P. B., Shim, H., & Cooper, L. A. D. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27166 · Cited by 84
Metadata
- Authors
- Dai, W., Dai, C., Qu, S., Li, J., & Das, S.
- Venue
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 421-425
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2529337537
- Cited by
- 357
- References
- 21
- FWCI
- 26.44
- Type
- conference paper
- Language
- en
- Open access
- green
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