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ORCIDMorten Kolbæk
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- affiliation: Aalborg University, Denmark
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2020 – today
- 2021
[i8]Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen, Jesper Jensen:
On TasNet for Low-Latency Single-Speaker Speech Enhancement. CoRR abs/2103.14882 (2021)- 2020
- [j4]Morten Kolbæk
, Zheng-Hua Tan, Søren Holdt Jensen, Jesper Jensen:
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 28: 825-838 (2020) - [c5]Mathias Bach Pedersen, Morten Kolbæk, Asger Heidemann Andersen, Søren Holdt Jensen, Jesper Jensen:
End-to-End Speech Intelligibility Prediction Using Time-Domain Fully Convolutional Neural Networks. INTERSPEECH 2020: 1151-1155
2010 – 2019
- 2019
- [j3]Morten Kolbaek, Zheng-Hua Tan, Jesper Jensen:
On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 27(2): 283-295 (2019) - [i7]Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen, Jesper Jensen:
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement. CoRR abs/1909.01019 (2019) - 2018
- [c4]Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
Monaural Speech Enhancement Using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure. ICASSP 2018: 5059-5063 - [i6]Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
Monaural Speech Enhancement using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure. CoRR abs/1802.00604 (2018) - [i5]Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
On the Equivalence between Objective Intelligibility and Mean-Squared Error for Deep Neural Network based Speech Enhancement. CoRR abs/1806.08404 (2018) - [i4]Morten Kolbæk:
Single-Microphone Speech Enhancement and Separation Using Deep Learning. CoRR abs/1808.10620 (2018) - 2017
- [j2]Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems. IEEE ACM Trans. Audio Speech Lang. Process. 25(1): 149-163 (2017) - [j1]Morten Kolbaek, Dong Yu, Zheng-Hua Tan, Jesper Jensen:
Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks. IEEE ACM Trans. Audio Speech Lang. Process. 25(10): 1901-1913 (2017) - [c3]Dong Yu, Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
Permutation invariant training of deep models for speaker-independent multi-talker speech separation. ICASSP 2017: 241-245 - [c2]Morten Kolbaek, Dong Yu, Zheng-Hua Tan, Jesper Jensen:
Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training. MLSP 2017: 1-6 - [i3]Morten Kolbæk, Dong Yu, Zheng-Hua Tan, Jesper Jensen:
Multi-talker Speech Separation and Tracing with Permutation Invariant Training of Deep Recurrent Neural Networks. CoRR abs/1703.06284 (2017) - [i2]Morten Kolbæk, Dong Yu, Zheng-Hua Tan, Jesper Jensen:
Joint Separation and Denoising of Noisy Multi-talker Speech using Recurrent Neural Networks and Permutation Invariant Training. CoRR abs/1708.09588 (2017) - 2016
- [c1]Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
Speech enhancement using Long Short-Term Memory based recurrent Neural Networks for noise robust Speaker Verification. SLT 2016: 305-311 - [i1]Dong Yu, Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen:
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation. CoRR abs/1607.00325 (2016)
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