Progressive loss functions for speech enhancement with deep neural networks

The progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies to improve the accuracy of speech enhancement solutions based on this mechanism. This paper studies the progressive speech enhancement using convolutional and residual neural network architectures and explores two criteria for loss function optimization: weighted and uniform progressive. This work carries out the evaluation on simulated and real speech samples with reverberation and added noise using REVERB and VoiceHome datasets. Experimental results show a variety of achievements among the loss function optimization criteria and the network architectures. Results show that the progressive design strengthens the model and increases the robustness to distortions due to reverberation and noise

Speech dereverberation and speaker separation using microphone arrays in realistic environments

This thesis concentrates on comparing novel and existing dereverberation and speaker separation techniques using multiple corpora, including a new corpus collected using a microphone array. Many corpora currently used for these techniques are recorded using head-mounted microphones in anechoic chambers. This novel corpus contains recordings with noise and reverberation made in office and workshop environments. Novel algorithms present a different way of approximating the reverberation, producing results that are competitive with existing algorithms. Dereverberation is evaluated using seven correlation-based algorithms and applied to two different corpora. Three of these are novel algorithms (Hs NTF, Cauchy WPE and Cauchy MIMO WPE). Both non-learning and learning algorithms are tested, with the learning algorithms performing better. For single and multi-channel speaker separation, unsupervised non-negative matrix factorization (NMF) algorithms are compared using three cost functions combined with sparsity, convolution and direction of arrival. The results show that the choice of cost function is important for improving the separation result. Furthermore, six different supervised deep learning algorithms are applied to single channel speaker separation. Historic information improves the result. When comparing NMF to deep learning, NMF is able to converge faster to a solution and provides a better result for the corpora used in this thesis

voiceHome-2 corpus - localization and speech enhancement baseline - code

Baseline scripts for localization and speech enhancement for the voiceHome-2 corpus, a corpus dedicated to distant-microphone speech processing in domestic environments that is available at https://zenodo.org/record/1252143 and described in the paper N. Bertin, E. Camberlein, R. Lebarbenchon, E. Vincent, S. Sivasankaran, I. Illina and F. Bimbot: VoiceHome-2, an extended corpus for multichannel speech processing in real homes, Speech Communication, Elsevier : North-Holland, 2019, 106, pp.68-78. ⟨10.1016/j.specom.2018.11.002⟩

VoiceHome-2, an extended corpus for multichannel speech processing in real homes

International audienceWe present a new, extended version of the voiceHome corpus for distant-microphone speech processing in domestic environments. This 5-hour corpus includes short reverberated, noisy utterances (smart home commands) spoken in French by 12 native French talkers in diverse realistic acoustic conditions and recorded by an 8-microphone device at various angles and distances and in various noise conditions. Noise-only segments before and after each utterance are included in the recordings. Clean speech and spontaneous speech recorded in 12 real rooms distributed in 4 different homes are also available. All data have been fully annotated. At last, we provide baseline software for speaker and noise localization, enhancement by source separation, and automatic speech recognition. This corpus stands apart from other corpora in the field by the number of rooms and homes considered and by the fact that it is publicly available at no cost. We describe the corpus specifications and annotations and the data recorded so far, and we report baseline results