@inproceedings{dicarlo2020blaster, author={Diego {Di Carlo} and Clement {Elvira} and Antoine {Deleforge} and Nancy {Bertin} and Remi {Gribonval}}, booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Blaster: An Off-Grid Method for Blind and Regularized Acoustic Echoes Retrieval}, year={2020}, volume={}, number={}, pages={156-160} }

Acoustic echoes retrieval is a research topic that is gaining importance in many speech and audio signal processing applications such as speech enhancement, source separation, dereverberation and room geometry estimation. This work proposes a novel approach to retrieve acoustic echoes timing off-grid and blindly, i.e., from a stereophonic recording of an unknown sound source such as speech. It builds on the recent framework of continuous dictionaries. In contrast with existing methods, the proposed approach does not rely on parameter tuning nor peak picking techniques by working directly in the parameter space of interest. The accuracy and robustness of the method are assessed on challenging simulated setups with varying noise and reverberation levels and are compared to two state-of-the-art methods.

@inproceedings{DiCarlo2019mirage, arxiv = {1906.08968}, author = { Di Carlo, Diego and Deleforge, Antoine and Bertin, Nancy}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, doi = {10.1109/ICASSP.2019.8683534}, hal_id = {hal-01909531}, keywords = {Image Microphones,Sound Source Localization,Supervised Learning,TDOA Estimation}, pages = {775--779}, title = {Mirage: 2D Source Localization Using Microphone Pair Augmentation with Echoes}, url = {https://github.com/Chutlhu/MIRAGE}, volume = {2019-May}, year = {2019} }

It is commonly observed that acoustic echoes hurt performance of sound source localization (SSL) methods. We introduce the concept of microphone array augmentation with echoes (MIRAGE) and show how estimation of early-echo characteristics can in fact benefit SSL. We propose a learning based scheme for echo estimation combined with a physics based scheme for echo aggregation. In a simple scenario involving 2 microphones close to a reflective surface and one source, we show using simulated data that the proposed approach performs similarly to a correlation-based method in azimuth estimation while retrieving elevation as well from 2 microphones only, an impossible task in anechoic settings.

@inproceedings{Scheibler2017separake, arxiv = {1711.06805}, author = {Scheibler, Robin and Di Carlo, Diego and Deleforge, Antoine and Dokmanic, Ivan}, doi = {10.1109/ICASSP.2018.8461345}, hal_id = {hal-01909531}, journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, keywords = {Echoes,Multi-channel,NMF,Room geometry,Source separation}, pages = {6897--6901}, title = {Separake: Source Separation with a Little Help from Echoes}, url = {https://github.com/fakufaku/separake}, year = {2018} }

It is commonly believed that multipath hurts various audio process-ing algorithms.At odds with this belief, we show that multipath in fact helps sound source separation,even with very simple propagation models.Unlike most existing methods, we neither ignore the room impulse responses, nor we attempt to estimate them fully. Weather assume that we know the positions of a few virtual micro-phones generated by echoes and we show how this gives us enough spatial diversity to get a performance boost over the anechoic case.We show improvements for two standard algorithms\u2014one that uses only magnitudes of the transfer functions, and one that also uses the phases.Concretely, we show that multichannel non-negative matrix factorization aided with a small number of echoes beats the vanilla variant of the same algorithm, and that with magnitude information only, echoes enable separation where it was previously impossible