Wind Noise Reduction using Non-negative Sparse Coding

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

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    We 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.
    Original languageEnglish
    Title of host publicationProceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP : Machine Learning for Signal Processing 17
    Publication date2007
    Article number4414345
    ISBN (Print)978-1-4244-1566-3
    Publication statusPublished - 2007
    Event2007 IEEE International Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece
    Duration: 27 Aug 200729 Aug 2007


    Workshop2007 IEEE International Workshop on Machine Learning for Signal Processing
    Internet address

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