Fetching the paper…
Reading the bibliography…
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality.
A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , 2001
2001
Earlier work this paper cites.
R. Huber and B. Kollmeier, “Pemo-q – a new method for objective audio quality assessment using a model of auditory perception,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 14, no. 6, 2006
2006
Earlier work this paper cites.
E. Vincent, R. Gribonval, and C. Févotte, “Performance measurement in blind audio source separation,” IEEE Transactions on Audio, Speech and Language Processing , vol. 14, no. 4, Jul. 2006
2006
Earlier work this paper cites.
P. C. Loizou, Speech Enhancement: Theory and Practice . CRC Press, 2007
2007
Earlier work this paper cites.
C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “A short-time objective intelligibility measure for time-frequency weighted noisy speech,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , 2010
2010
Earlier work this paper cites.
V. Emiya, E. Vincent, N. Harlander, and V. Hohmann, “Subjective and objective quality assessment of audio source separation,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 7, 2011
2011
Earlier work this paper cites.
X. Lu, Y. Tsao, S. Matsuda, and C. Hori, “Speech enhancement based on deep denoising autoencoder,” in Proc. ISCA Interspeech , 2013
2013
Earlier work this paper cites.
F. J. Weninger, J. R. Hershey, J. Le Roux, and B. Schuller, “Discriminatively trained recurrent neural networks for single-channel speech separation,” in Proc. GlobalSIP Machine Learning Applications in Speech Processing Symposium , 2014
2014
Earlier work this paper cites.
Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, “An experimental study on speech enhancement based on deep neural networks,” IEEE Signal Processing Letters , vol. 21, no. 1, 2014
2014
Earlier work this paper cites.
C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, D. P. Ellis, and C. C. Raffel, “mir_eval: A transparent implementation of common mir metrics,” in Proc. International Society for Music Information Retrieval Conference (ISMIR) , 2014
2014
Cited alongside, same era.
H. Erdogan, J. R. Hershey, S. Watanabe, and J. Le Roux, “Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Apr. 2015
2015
Cited alongside, same era.
F. Weninger, H. Erdogan, S. Watanabe, E. Vincent, J. Le Roux, J. R. Hershey, and B. Schuller, “Speech enhancement with LSTM recurrent neural networks and its application to noise-robust ASR,” in Proc. International Conference on Latent Variable Analysis and Signal Separation (LVA) , 2015
2015
Cited alongside, same era.
J. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: Discriminative embeddings for segmentation and separation,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Mar. 2016
2017
Later among the works it cites.
2017
Later among the works it cites.
Z.-Q. Wang, J. Le Roux, and J. R. Hershey, “Alternative objective functions for deep clustering,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Apr. 2018
2018
Closest in time.
F.-R. Stöter, A. Liutkus, and N. Ito, “The 2018 signal separation evaluation campaign,” in Proc. International Conference on Latent Variable Analysis and Signal Separation (LVA) , 2018
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Y. Isik, J. Le Roux, Z. Chen, S. Watanabe, and J. R. Hershey, “Single-channel multi-speaker separation using deep clustering,” in Proc. ISCA Interspeech , Sep. 2016
2016
Cited alongside, same era.
D. Yu, M. Kolbæk, Z.-H. Tan, and J. Jensen, “Permutation invariant training of deep models for speaker-independent multi-talker speech separation,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Mar. 2017
2017
Cited alongside, same era.
M. Kolbæk, D. Yu, Z.-H. Tan, and J. Jensen, “Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 25, no. 10, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Y. Luo, Z. Chen, J. R. Hershey, J. Le Roux, and N. Mesgarani, “Deep clustering and conventional networks for music separation: Stronger together,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , 2017
2017
Cited alongside, same era.
Z.-Q. Wang, J. Le Roux, D. Wang, and J. R. Hershey, “End-to-end speech separation with unfolded iterative phase reconstruction,” in Proc. ISCA Interspeech , Sep. 2018
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.