Projects per year
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- 6 Similar Profiles
Speech enhancement
Engineering & Materials Science
Speech intelligibility
Engineering & Materials Science
intelligibility
Physics & Astronomy
Mean square error
Engineering & Materials Science
augmentation
Physics & Astronomy
estimators
Physics & Astronomy
Cost functions
Engineering & Materials Science
envelopes
Physics & Astronomy
Network
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Projects 2015 2017
- 1 Finished
OCTAVE: Objective Control for TAlker VErification
Tan, Z., Sarkar, A. K., Thomsen, D. A. L., Yu, H., Kolbæk, M. & Thomsen, N. B.
01/06/2015 → 31/07/2017
Project: Research
Research Output 2016 2020
1
Citation
(Scopus)
62
Downloads
(Pure)
On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement
Kolbæk, M., Tan, Z-H. & Jensen, J., Feb 2019, In : IEEE/ACM Transactions on Audio, Speech, and Language Processing. 27, 2, p. 283-295 13 p., 8509159.Research output: Contribution to journal › Journal article › Research › peer-review
Open Access
File
Speech intelligibility
Speech enhancement
intelligibility
Mean square error
augmentation
12
Citations
(Scopus)
Monaural Speech Enhancement using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure
Kolbæk, M., Tan, Z-H. & Jensen, J., 2018, International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, p. 5059-5063 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
Speech enhancement
Speech intelligibility
Cost functions
Mean square error
Deep neural networks
Single-Microphone Speech Enhancement and Separation Using Deep Learning
Kolbæk, M., 2018, Aalborg Universitetsforlag. 233 p. (Ph.d.-serien for Det Tekniske Fakultet for IT og Design, Aalborg Universitet).Research output: Book/Report › Ph.D. thesis › Research
Open Access
File
108
Citations
(Scopus)
Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks
Kolbæk, M., Yu, D., Tan, Z-H. & Jensen, J., 13 Jul 2017, In : I E E E Transactions on Audio, Speech and Language Processing. 25, 10, p. 1901-1913 13 p.Research output: Contribution to journal › Journal article › Research › peer-review
118
Citations
(Scopus)
Permutation invariant training of deep models for speaker-independent multi-talker speech separation
Yu, D., Kolbæk, M., Tan, Z-H. & Jensen, J., 19 Jun 2017, International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, p. 241 - 245Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
Datasets
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement
Kolbæk, M. (Creator), VBN, 1 Jan 2020
Dataset
File
Activities 2018 2018
- 1 Internal examination
Single-Microphone Speech Enhancement and Separation Using Deep Learning
Thomas Arildsen (Opponent)30 Nov 2018
Activity: Examination › Internal examination
Press / Media
Kunstige neurale netværk skal gøre livet lettere for høreapparatbrugere
Morten Kolbæk, Jesper Jensen & Zheng-Hua Tan
04/01/2019 → 07/01/2019
17 items of Media coverage, 1 Media contribution
Press/Media: Press / Media