Publication: Impact of low-precision deep regression networks on single-channel source separation
Impact of low-precision deep regression networks on single-channel source separation
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Ceolini, E., & Liu, S.-C. (2017, March 9). Impact of low-precision deep regression networks on single-channel source separation. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP2017, New Orleans LA. https://doi.org/10.1109/ICASSP.2017.7952157
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Recent work on developing training methods for reduced precision Deep Convolutional Networks show that these networks can perform with similar accuracy to full precision networks when tested on a classification task. Reduced precision networks decrease the demand on the memory and computational power capabilities of the computing platform. This paper investigates the impact of reduced precision deep Recurrent Neural Networks (RNNs) when trained on a regression task, in this case, a monaural source separation task. The effect of reduce
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- Ceolini, Enea
- Liu, Shih-Chii
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Ceolini, E., & Liu, S.-C. (2017, March 9). Impact of low-precision deep regression networks on single-channel source separation. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP2017, New Orleans LA. https://doi.org/10.1109/ICASSP.2017.7952157
