Data Augmenting Contrastive Learning of Speech Representationsin the Time Domain
Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation.
['[EMPTY]', 'Within spk. dev', 'Within spk. dev', 'Across spk. dev', 'Across spk. dev']
We discovered accidentally that for additive noise, low frequencies are more effective than high frequencies. We therefore explored systematically the effect of the spectral characteristics of noise by filtering sounds from the MUSAN dataset [musan2015] in successive frequency bands. We selected 5 broad bands, defined by 4 cutoff points by the tripling of the frequency: 80Hz, 240Hz, 720Hz, 2160Hz). We found that the optimal additive noise was obtained by bandpass filtering MUSAN sounds in the [80,240] Here, we explore how frequency filtering affects additive noise data augmentation. We did two experiments: band-pass filtering, and lowpass filtering. For bandpass, here are the frequency bands we applied to the MUSAN dataset: [0,80] Hz, [80,240] Hz, [240,720] Hz, [720,2060] Hz, [2160−8000] Hz. The second band corresponds roughly to the range of human pitch (F0), the third, to the range of the first formant (F1), the fourth to the range of the second formant (F2). The extreme ranges (very low or very high frequencies) do not typically carry much information. An optimal range seems to be [80,240] Hz. For lowpass, we selected sucessive 100Hz bands, starting from zero.