Speech Enhancement

RPCA (12 papers)

Z. Chen, D. Ellis, “Speech enhancement by sparse, low-rank, and dictionary spectrogram decomposition”, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, USA, October 2013.

Z. Chen, B. Mac Fee, D. Ellis, "Speech enhancement by low-rank and convolutive dictionary spectrogram decomposition", InterSpeech 2014, 2014.

C. Sun, Q. Zhang, J. Wang, J. Xie, ”Noise reduction based on robust principal component analysis”, Journal of Computational Information Systems, Volume 10, No. 10, pages 4403-4410, 2014.

C. Sun, Q. Zhu, M. Wan, “A novel speech enhancement method based on constrained low-rank and sparse matrix decomposition”, Speech Communication, Volume 60, pages 44–55, 2014.

C. Sun, J. Xie, Y. Leng,“A Signal Subspace Speech Enhancement Approach Based on Joint Low-Rank and Sparse Matrix Decomposition”, Archives of acoustics, Volume 41, No. 2, pages 245–254, 2016.

S. Yuan, C. Sun, "Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition", EITCE 2017, 2017.

M. Gavrilescu, “Noise robust automatic speech recognition system by integrating Robust Principal Component Analysis (RPCA) and exemplar-based sparse representation”, International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2015, Bucharest, Romania, 2015.

C. Wu, H. Hsu, S. Wang, J. Hung, Y. Lai, H. Wang, Y. Tsao, “Wavelet speech enhancement based on robust principal component analysis”, InterSpeech 2017, Stockholm, Sweden, 2017.

M. Fan, L. Liu, W. Li, “Speech dereverberation based on sparse matrix decomposition”, International Conference on Information Technology and Management Innovation, ICITMI 2015, 2015.

S. Mavaddaty, S. Ahadi, S. Seyedin, “A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation, Speech Communication, Volume 76, pages 42–60, 2016.

W. Shi, X. Zhang, X. Zou, W. Han, G. Min, "Auditory mask estimation by RPCA for monaural speech enhancement”, IEEE/ACIS 16th International Conference on Computer and Information Science, ICIS 2017, pages 179-184, Wuhan, China, 2017.

C. Sun, C. Yuan, "Speech Enhancement based on Constrained Low-rank Sparse Matrix Decomposition Integrated with Temporal Continuity Regularisation", Archives of Acoustics, Volume 44, No. 4, pages 681-692, 2019.

Stable RPCA (3 papers)

J. Huang, X. Zhang, Y. Zhang, X. Zou, L. Zeng, “Speech denoising via low-rank and sparse matrix decomposition”, ETRI Journal, Volume 36, Number 1, February 2014.

P. Sun, J. Qin, “Low rank and sparsity analysis applied to speech enhancement via online estimated dictionary”, Preprint, 2016.

Z. Chen, B. Mc Fee, D. Ellis, “Speech enhancement by low-rank and convolutive dictionary spectrogram decomposition”, InterSpeech 2014, 2014.

ORPCA (1 paper)

Y. Bando, K. Itoyama, M. Konyo, S. Tadokoro, K. Nakadai, K. Yoshii, H. Okuno, “Human-Voice Enhancement based on Online RPCA for a Hose-shaped Rescue Robot with a Microphone Array”, IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2015, 2015.

Bayesian RPCA (1 paper)

Y. Bando, K. Itoyama, M. Konyo, S. Tadokoro, K. Nakadai, K. Yoshii, T. Kawahara, H. Okuno, “Speech Enhancement Based on Bayesian Low-Rank and Sparse Decomposition of Multichannel Magnitude Spectrograms”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Volume 26, No. 2, pages 215–230, 2018.

RNMF (2 papers)

M. Sun, Y. Li, J. Gemmeke, “Speech enhancement under low SNR conditions via noise estimation using sparse and low-rank NMF with Kullback-Leibler divergence”, IEEE Transactions on Speech and Audio Processing, Volume 23, Issue 7, pages 1233–1242, 2015.

Y. Bando, K. Itoyama, M. Konyo, S. Tadokoro, K. Nakadai, K. Yoshii, H. Okuno, “Variational Bayesian Multi-channel Robust NMF for Human-voice Enhancement with a Deformable and Partially-occluded Microphone Array”, European Signal Processing Conference, EUSIPCO 2016. pages 1018-1022, 2016.