首页> 外文期刊>IEEE transactions on audio, speech and language processing >Multichannel blind deconvolution for source separation in convolutive mixtures of speech
【24h】

Multichannel blind deconvolution for source separation in convolutive mixtures of speech

机译:多通道盲解卷积用于语音卷积混合中的源分离

获取原文
获取原文并翻译 | 示例

摘要

This paper addresses the blind separation of convolutive and temporally correlated mixtures of speech, through the use of a multichannel blind deconvolution (MBD) method. In the proposed framework (LP-NGA), spatio-temporal separation is carried out by entropy maximization using the well-known natural gradient algorithm (NGA), while a temporal pre-whitening stage, based on linear prediction (LP), manages to fully preserve the original spectral characteristics of each source contribution. Confronted with synthetic convolutive mixtures, we show that the LP-NGA-an unconstrained natural extension to the multichannel BSS problem-benefits not only from fewer model constraints, but also from other factors, such as an overall increase in separation performance, spectral preservation efficiency and speed of convergence.
机译:本文通过使用多通道盲解卷积(MBD)方法解决了卷积和时间相关的语音混合的盲分离。在提出的框架(LP-NGA)中,时空分离是通过使用众所周知的自然梯度算法(NGA)进行熵最大化来实现的,而基于线性预测(LP)的时间预白化阶段则可以充分保留每个光源贡献的原始光谱特征。面对合成卷积混合物,我们表明LP-NGA是对多通道BSS问题的无限制自然扩展,不仅来自较少的模型约束,还来自其他因素,例如分离性能,光谱保存效率的整体提高和收敛速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号