Currently, most speech processing techniques use magnitude spectrograms as frontend and are therefore by default discarding part of the signal: the phase. In order to overcome this limitation, we propose an end-to-end learning method for speech denoising based on Wavenet. The proposed model adaptation retains Wavenet's powerful acoustic modeling capabilities, while significantly reducing its timecomplexity by eliminating its autoregressive nature. Specifically, the model makes use of non-causal, dilated convolutions and predicts target fields instead of a single target sample. The discriminative adaptation of the model we propose, learns in a supervised fashion via minimizing a regression loss. These modifications make the model highly parallelizable during both training and inference. Both computational and perceptual evaluations indicate that the proposed method is preferred to Wiener filtering, a common method based on processing the magnitude spectrogram.Previous discussion motivates our study in adapting Wavenet's model (an autoregressive generative model) for speech denoising. Our main hypothesis is that by learning multi-scale hierarchical representations from raw audio we can overcome the inherent limitations of using the magnitude * Contributed equally.

International audienceThis review covers recent developments relating to organocatalyzed transformations by chiral isothioureas (ITUs) since their original introduction by Birman in 2006. This class of nucleophilic heterocycles was first involved in anhydride activation in enantioselective acyl transfer reactions, but it was more recently shown that activation of other reagents was possible, considerably enlarging their number of catalytic enantioselective transformations. Four main modes of activation as Lewis bases can currently be listed: (1) acylisothiouronium intermediates involved in acyl transfer, (2) silylisothiouronium species involved in silyl transfer, (3) acylisothiouronium enolates involved in several concerted and formal pericyclic transformations, and (4) α,_-unsaturated acylisothiouronium species involved in domino transformations. This review is organized according to these different modes of activation of chiral isothioureas

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on over 2M tracks from YouTube videos and encompassing over 500 sound classes. However, AudioSet is not an open dataset as its official release consists of pre-computed audio features. Downloading the original audio tracks can be problematic due to YouTube videos gradually disappearing and usage rights issues. To provide an alternative benchmark dataset and thus foster SER research, we introduce FSD50K, an open dataset containing over 51k audio clips totalling over 100h of audio manually labeled using 200 classes drawn from the AudioSet Ontology. The audio clips are licensed under Creative Commons licenses, making the dataset freely distributable (including waveforms). We provide a detailed description of the FSD50K creation process, tailored to the particularities of Freesound data, including challenges encountered and solutions adopted. We include a comprehensive dataset characterization along with discussion of limitations and key factors to allow its audioinformed usage. Finally, we conduct sound event classification experiments to provide baseline systems as well as insight on the main factors to consider when splitting Freesound audio data for SER. Our goal is to develop a dataset to be widely adopted by the community as a new open benchmark for SER research.