20152020
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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

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Projects 2015 2017

OCTAVE: Objective Control for TAlker VErification

Tan, Z., Sarkar, A. K., Thomsen, D. A. L., Yu, H., Kolbæk, M. & Thomsen, N. B.

01/06/201531/07/2017

Project: Research

Research Output 2016 2020

  • 4 Article in proceeding
  • 4 Journal article
  • 1 Ph.D. thesis
1 Citation (Scopus)
62 Downloads (Pure)
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 proceedingArticle in proceedingResearchpeer-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/ReportPh.D. thesisResearch

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 journalJournal articleResearchpeer-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 - 245

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Activities 2018 2018

  • 1 Internal examination

Single-Microphone Speech Enhancement and Separation Using Deep Learning

Thomas Arildsen (Opponent)
30 Nov 2018

Activity: ExaminationInternal examination

Press / Media

Kunstige neurale netværk skal gøre livet lettere for høreapparatbrugere

Morten Kolbæk, Jesper Jensen & Zheng-Hua Tan

04/01/201907/01/2019

17 items of Media coverage, 1 Media contribution

Press/Media: Press / Media