Instrumental and perceptual evaluation of dereverberation techniques based on robust acoustic multichannel equalization

Speech signals recorded in an enclosed space by microphones at a distance from the speaker are often corrupted by reverberation, which arises from the superposition of many delayed and attenuated copies of the source signal. Because reverberation degrades the signal, removing reverberation would enhance quality. Dereverberation techniques based on acoustic multichannel equalization are known to be sensitive to room impulse response perturbations. In order to increase robustness, several methods have been proposed, as for example, using a shorter reshaping filter length, incorporating regularization, or applying a sparsity-promoting penalty function. This paper focuses on evaluating the performance of these methods for single-source multi-microphone scenarios, using instrumental performance measures as well as using subjective listening tests. By analyzing the correlation between the instrumental and the perceptual results, it is shown that signal-based performance measures are more advantageous than channel-based performance measures to evaluate the perceptual speech quality of signals that were dereverberated by equalization techniques. Furthermore, this analysis also demonstrates the need to develop more reliable instrumental performance measures

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure