@article{foy2021mean, title={Mean absorption estimation from room impulse responses using virtually supervised learning}, author={Foy, C{'e}dric and Deleforge, Antoine and Di Carlo, Diego}, journal={The Journal of the Acoustical Society of America}, volume={150}, number={2}, pages={1286--1299}, year={2021}, publisher={AIP Publishing} }

In the context of building acoustics and the acoustic diagnosis of an existing room, it introduces and investigates a new approach to estimate the mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually supervised learning, namely, the RIR-to-absorption mapping is implicitly learned by regression on a simulated dataset using artificial neural networks. Simple models based on well-understood architectures are the focus of this work. The critical choices of geometric, acoustic, and simulation parameters, which are used to train the models, are extensively discussed and studied while keeping in mind the conditions that are representative of the field of building acoustics. Estimation errors from the learned neural models are compared to those obtained with classical formulas that require knowledge of the room's geometry and reverberation times. Extensive comparisons made on a variety of simulated test sets highlight different conditions under which the learned models can overcome the well-known limitations of the diffuse sound field hypothesis underlying these formulas. Results obtained on real RIRs measured in an acoustically configurable room show that at 1 kHz and above, the proposed approach performs comparably to classical models when reverberation times can be reliably estimated and continues to work even when they cannot.

@article{carlo2021dechorate, title={dEchorate: a calibrated room impulse response dataset for echo-aware signal processing}, author={Carlo, Diego Di and Tandeitnik, Pinchas and Foy, Cedri{'c} and Bertin, Nancy and Deleforge, Antoine and Gannot, Sharon}, journal={EURASIP Journal on Audio, Speech, and Music Processing}, volume={2021}, pages={1--15}, year={2021}, publisher={Springer} }

This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks.

@article{Deleforge2019audio, author = {Deleforge, Antoine and {Di Carlo}, Diego and Strauss, Martin and Serizel, Romain and Marcenaro, Lucio}, journal = {IEEE Signal Processing Magazine}, number = {5}, pages = {138--144}, publisher = {IEEE}, title = {Audio-Based Search and Rescue With a Drone: Highlights From the IEEE Signal Processing Cup 2019 Student Competition [SP Competitions]}, url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8827999}, volume = {36}, year = {2019} }

Increasing interest in unmanned aerial vehicles (UAVs), commonly referred to as drones, has occurred in recent years. Search and rescue scenarios where humans in emergency situations need to be quickly found in difficult to access areas constitute an important field of application for this technology. Drones have already been used by humanitarian organizations in countries such as Haiti and the Philippines to map areas after a natural disaster using high-resolution embedded cameras, as documented in a recent United Nations report [1]. Although research efforts have focused mostly on developing video-based solutions for this task [2], UAV-embedded audio-based localization has received relatively less attention [3-7]. However, UAVs equipped with a microphone array could be of critical help to localize people in emergency situations, especially when video sensors are limited by a lack of visual feedback due to bad lighting conditions (such as at night or in fog) or obstacles limiting the field of view (Figure 1).