Regularized Non-negative Matrix Factorization with Temporal Dependencies for Speech Denoising

机译：用时间依赖性进行正常的非负矩阵分解，用于语音去噪

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We present a tecchnique for denoising speech using temporally regularized nonnegative matrix factorization (NMF). In previous work [1], we used a regularized NMF update to impose structure within each audio frame. In this paper, we add frametoframe regularization across time and show that this additional regularization can also improve our speech denoising results. We evaluate our algorithm on a range of nonstationary noise types and outperform a state-of-the-art Wiener filter implementation.

机译：我们使用时间正常化的非负面矩阵分解（NMF）来提出一种用于去噪的TECCHNIQUE。在以前的工作[1]中，我们使用了正则化的NMF更新来强加在每个音频帧内的结构。在本文中，我们在跨时间添加FrameToframe正规化，并显示这种额外的正则化也可以提高我们的语音去噪结果。我们在一系列非营养噪声类型和优于最先进的维纳滤波器实现中评估我们的算法。

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《International Speech Communication Association》|2008年||共4页
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Kevin W. Wilson; Bhiksha Raj; Paris Smaragdis;
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中图分类 TN912.3-532;
关键词
speech enhancement; source separation; speech modeling; speech processing;

机译：语音增强;源分离;语音建模;语音处理;

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2. Non-negative matrix factorization for speech/music separation using source dependent decomposition rank, temporal continuity term and filtering [J] . Abdali S., NaserSharif B. Biomedical signal processing and control . 2017,第Jula期

机译：使用源依赖的分解等级，时间连续性项和滤波进行语音/音乐分离的非负矩阵分解
3. An incremental algorithm based on multichannel non-negative matrix partial co-factorization for ambient denoising in auscultation [J] . De La Torre Cruz Juan, Canadas Quesada Francisco Jesus, Martinez-Munoz Damian, Applied Acoustics . 2021,第Nova期

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4. Regularized Non-negative Matrix Factorization with Temporal Dependencies for Speech Denoising [C] . Kevin W. Wilson, Bhiksha Raj, Paris Smaragdis International Speech Communication Association . 2008

机译：用时间依赖性进行正常的非负矩阵分解，用于语音去噪
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7. Speech Denoising Using Non-Negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints [O] . Ludeña-Choez Jimmy, Gallardo-Antolín Ascensión 2012

机译：使用具有Kullback-Leibler发散和稀疏约束的非负矩阵分解进行语音去噪

Regularized Non-negative Matrix Factorization with Temporal Dependencies for Speech Denoising

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