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Ultrasound-coupled semi-supervised nonnegative matrix factorisation for speech enhancement

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Research areas:
Year:
2014
Type of Publication:
In Proceedings
Keywords:
Nonnegative Matrix Factorisation, Ultrasound, Acoustic Doppler Sensor, Source Separation
Authors:
Barker, Tom; Virtanen, Tuomas; Delhomme, Olivier
Book title:
Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, May 2014.
Month:
May
Abstract:
We present an extension to an existing speech enhancement technique, whereby the incorporation of easily obtained Doppler-based ultrasound data, obtained from frequency shifts caused by a talker’s mouth movements, is shown to improve speech enhancement results. Noisy speech mixtures were enhanced using semi-supervised nonnegative matrix factorisation (NMF). Ultrasound data recorded alongside the speech is transformed into the spectral domain and used additionally to audio in the mixture to be separated. Speech components are learned from a training set, whilst noise components are estimated from the mixture signal. We show that the ultrasound data can improve source-to-distortion ratios for the enhanced speech, relative to both the non-ultrasound NMF case and an established Wiener filter-based speech enhancement method.
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