Wind Noise Reduction using Non-negative Sparse Coding

Mikkel N. Schmidt, Jan Larsen, Fu-Tien Hsiao

AbstractWe introduce a new speaker independent method for reducing
wind noise in single-channel recordings of noisy speech. The
method is based on non-negative sparse coding and relies on a
wind noise dictionary which is estimated from an isolated noise
recording. We estimate the parameters of the model and discuss
their sensitivity. We then compare the algorithm with the classical
spectral subtraction method and the Qualcomm-ICSI-OGI
noise reduction method. We optimize the sound quality in terms of
signal-to-noise ratio and provide results on a noisy speech recognition
task.
TypeConference paper [With referee]
ConferenceIEEE International Workshop on Machine Learning for Signal Processing
EditorsKonstantinos Diamantaras, Tülay Adali, Ioannis Pitas, Jan Larsen, Theophilos Papadimitriou, Scott Douglas
Year2007    Month August    pp. 431-436
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
ISBN / ISSNISBN: 1-4244-1566-7
NoteThis publication includes a supplementary windnoise database [zip file] used in the publication
Electronic version(s)[pdf] [zip]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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