WHAM!: Extending Speech Separation to Noisy Environments
Table 4: SI-SDR [dB] comparison of our implementations of other benchmark networks on the WHAM! separate-clean and separate-noisy tasks
['Model', 'Dataset', 'separate-clean Output', 'separate-clean Δ', 'separate-noisy Output', 'separate-noisy Δ']
We see that while TasNet significantly outperformed chimera++ in the clean case, it did not perform as well under noisy conditions. We suspect this is because learning directly from waveforms is more difficult with noisy signals. Like chimera++, it operates directly on the spectrogram, and while performance in terms of SI-SDR is not as high as chimera++, it trains much faster and uses fewer parameters.