{"title":"ICA-based Noise Reduction for Mobile Phone Speech Communication","authors":"Zhipeng Zhang, M. Etoh","doi":"10.1109/ICCCN.2007.4317863","DOIUrl":null,"url":null,"abstract":"We propose a frequency-domain independent component analysis (ICA) with robust and computationally-light post processing method for background noise reduction in mobile phone speech communication. In our scenario, multi-source signal separation is not the target, but noise reduction is the primal one. This primal target characterizes our approach that promotes a new physical constraint, in other words, we place a restriction on the amplitude range of the transfer functions rather than assuming that the amplitudes are constant. When there are diffraction, obstacles and reflections in the real-world environment, it is better to assume that transfer function amplitude (derived from the distance to the mouth) varies within a certain range. Our two-microphone experiment shows that the ICA-based noise reduction significantly improves speech recognition performance especially in severe noise conditions.","PeriodicalId":388763,"journal":{"name":"2007 16th International Conference on Computer Communications and Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 16th International Conference on Computer Communications and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCN.2007.4317863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
We propose a frequency-domain independent component analysis (ICA) with robust and computationally-light post processing method for background noise reduction in mobile phone speech communication. In our scenario, multi-source signal separation is not the target, but noise reduction is the primal one. This primal target characterizes our approach that promotes a new physical constraint, in other words, we place a restriction on the amplitude range of the transfer functions rather than assuming that the amplitudes are constant. When there are diffraction, obstacles and reflections in the real-world environment, it is better to assume that transfer function amplitude (derived from the distance to the mouth) varies within a certain range. Our two-microphone experiment shows that the ICA-based noise reduction significantly improves speech recognition performance especially in severe noise conditions.