Learning Multiscale Features Directly From Waveforms
Table 2: Single scale waveform convolution outperforms the spectrogram baseline at low strides. The trend is visualized in Figure 2.
['Type', 'Spectrogram\xa0/\xa0Convolution # Features', 'Spectrogram\xa0/\xa0Convolution Window', 'Spectrogram\xa0/\xa0Convolution Stride', 'Pooling Stride', 'WER(%)']
We decided to test this hypothesis that a convolution with low stride and pooling can find at least as good a basis as the spectrogram. For each decrease in stride of the convolution, the stride of the pooling is increased to give a consistent total stride of 20ms, which is the same as the stride of the spectrogram with pooling. At comparable strides to the spectrogram (10ms) the convolution is unable to perform as well, likely due to needing to represent phase shifts as well as frequency variation. However, as the stride dips below 2ms, the convolution asymptotically reaches a superior level of performance to the spectrogram. To be fair, this improved performance comes at increased computational cost and memory usage.