Wind Noise Reduction using Non-negative Sparse Coding

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

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    1244 Downloads (Pure)

    Abstract

    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
    PublisherIEEE
    Publication date2007
    Pages431-436
    Article number4414345
    ISBN (Print)978-1-4244-1566-3
    DOIs
    Publication statusPublished - 2007
    Event2007 IEEE International Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece
    Duration: 27 Aug 200729 Aug 2007
    http://mlsp2007.conwiz.dk/

    Workshop

    Workshop2007 IEEE International Workshop on Machine Learning for Signal Processing
    Country/TerritoryGreece
    CityThessaloniki
    Period27/08/200729/08/2007
    Internet address

    Bibliographical note

    Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Fingerprint

    Dive into the research topics of 'Wind Noise Reduction using Non-negative Sparse Coding'. Together they form a unique fingerprint.

    Cite this