Morten Kolbæk — accepted AI-conference papers

2018

Monaural Speech Enhancement Using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure

Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen

ICASSP 2018accepted

In this paper we propose a Deep Neural Network (D NN) based Speech Enhancement (SE) system that is designed to maximize an approximation of the Short-Time Objective Intelligibility (STOI) measure. We formalize an approximate-STOI cost function and derive analytical expressions for the gradients requ…

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2017

Permutation invariant training of deep models for speaker-independent multi-talker speech separation

Dong Yu, Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen

ICASSP 2017accepted

We propose a novel deep learning training criterion, named permutation invariant training (PIT), for speaker independent multi-talker speech separation, commonly known as the cocktail-party problem. Different from the multi-class regression technique and the deep clustering (DPCL) technique, our nov…

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