Matthias Zöhrer

Representation learning for single-channel source separation and bandwidth extension

In this paper, we use deep representation learning for model-based single-channel source separation (SCSS) and artificial bandwidth extension (ABE). Both tasks are ill-posed and source-specific prior knowledge is required. In addition to well-known generative models such as restricted Boltzmann machines and higher order contractive autoencoders two recently introduced deep models, namely generative stochastic networks (GSNs) and sum-product networks (SPNs), are used for learning spectrogram representations. For SCSS we evaluate the deep architectures on data of the 2 nd CHiME speech separation challenge and provide results for a speaker dependent, a speaker independent, a matched noise condition and an unmatched noise condition task. GSNs obtain the best PESQ and overall perceptual score on average in all four tasks. Similarly, frame-wise GSNs are able to reconstruct the missing frequency bands in ABE best, measured in frequency-domain segmental SNR. They outperform SPNs embedded in hidden Markov models and the other representation models significantly.

Representation models in single channel source separation

Model-based single-channel source separation (SCSS) is an ill-posed problem requiring source-specific prior knowledge. In this paper, we use representation learning and compare general stochastic networks (GSNs), Gauss Bernoulli restricted Boltzmann machines (GBRBMs), conditional Gauss Bernoulli restricted Boltzmann machines (CGBRBMs), and higher order contractive autoencoders (HCAEs) for modeling the source-specific knowledge. In particular, these models learn a mapping from speech mixture spectrogram representations to single-source spectrogram representations, i.e. we apply them as filter for the speech mixture. In the test case, the individual source spectrograms of both models are inferred and the softmask for re-synthesis of the time signals is determined thereof. We evaluate the deep architectures on data of the 2nd CHiME speech separation challenge and provide results for a speaker dependent, a speaker independent, a matched noise condition and an unmatched noise condition task. Our experiments show the best PESQ and overall perceptual score on average for GSNs in all four tasks.

Multi-channel speech processing architectures for noise robust speech recognition: 3rd CHiME challenge results

Recognizing speech under noisy condition is an ill-posed problem. The CHiME 3 challenge targets robust speech recognition in realistic environments such as street, bus, caffee and pedestrian areas. We study variants of beamformers used for pre-processing multi-channel speech recordings. In particular, we investigate three variants of generalized side-lobe canceller (GSC) beamformers, i.e. GSC with sparse blocking matrix (BM), GSC with adaptive BM (ABM), and GSC with minimum variance distortionless response (MVDR) and ABM. Furthermore, we apply several post-filters to further enhance the speech signal. We introduce MaxPower postfilters and deep neural postfilters (DPFs). DPFs outperformed our baseline systems significantly when measuring the overall perceptual score (OPS) and the perceptual evaluation of speech quality (PESQ). In particular DPFs achieved an average relative improvement of 17.54% OPS points and 18.28% in PESQ, when compared to the CHiME 3 baseline. DPFs also achieved the best WER when combined with an ASR engine on simulated development and evaluation data, i.e. 8.98% and 10.82% WER. The proposed MaxPower beamformer achieved the best overall WER on CHiME 3 real development and evaluation data, i.e. 14.23% and 22.12%, respectively.

DNN-based speech mask estimation for eigenvector beamforming

In this paper, we present an optimal multi-channel Wiener filter, which consists of an eigenvector beamformer and a single-channel postfilter. We show that both components solely depend on a speech presence probability, which we learn using a deep neural network, consisting of a deep autoencoder and a softmax regression layer. To prevent the DNN from learning specific speaker and noise types, we do not use the signal energy as input feature, but rather the cosine distance between the dominant eigenvectors of consecutive frames of the power spectral density of the noisy speech signal. We compare our system against the BeamformIt toolkit, and state-of-the-art approaches such as the front-end of the best system of the CHiME3 challenge. We show that our system yields superior results, both in terms of perceptual speech quality and classification error.

Manual versus Automated: The Challenging Routine of Infant Vocalisation Segmentation in Home Videos to Study Neuro(mal)development.

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