DAFx 2018 Conference
Sep 4-8, 2018
Aveiro, Portugal
Alexey Lukin has presented a paper "Removing Lavalier Microphone Rustle With Recurrent Neural Networks".
Abstract
The noise that lavalier microphones produce when rubbing against clothing (typically referred to as rustle) can be extremely difficult to automatically remove because it is highly non-stationary and overlaps with speech in both time and frequency. Recent breakthroughs in deep neural networks have led to novel techniques for separating speech from non-stationary background noise. In this paper, we apply neural network speech separation techniques to remove rustle noise, and quantitatively compare multiple deep network architectures and input spectral resolutions. We find the best performance using bidirectional recurrent networks and spectral resolution of around 20 Hz. Furthermore, we propose an ambience preservation post-processing step to minimize potential gating artifacts during pauses in speech.
Audio Examples and Spectrograms
Male speech
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Features that can be observed on spectrograms:
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Hover a mouse over the algorithm name to show the spectrogram, click to download a WAV file
| Original speech recording with rustle | rustle clearly audible |
| De-Rustle algorithm | rustle attenuated, but excessive gating between phrases |
| De-Rustle with Ambience Preservation | rustle attenuated, room tone reintroduced |