A comparison between aeroacoustic source mapping techniques for the characterisation of wind turbine blade models with microphone arrays

<p class="Abstract">Characterising the aeroacoustic noise sources generated by a rotating wind turbine blade provides useful information for tackling noise reduction of this mechanical system. In this context, microphone array measurements and acoustic source mapping techniques are powerful tools for the identification of aeroacoustic noise sources. This paper discusses a series of acoustic mapping strategies that can be exploited in this kind of applications. A single-blade rotor was tested in a semi-anechoic chamber using a circular microphone array. <br />The Virtual Rotating Array (VRA) approach, which transforms the signals acquired by the physical static array into signals of virtual microphones synchronously rotating with the blade, hence ensuring noise-source stationarity, was used to enable the use of frequency domain acoustic mapping techniques. A comparison among three different acoustic mapping methods is presented: Conventional Beamforming, CLEAN-SC and Covariance Matrix Fitting based on Iterative Re-weighted Least Squares and Bayesian approach. The latter demonstrated to provide the best results for the application and made it possible a detailed characterization of the noise sources generated by the rotating blade at different operating conditions.</p>

SonicFace

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.

Towards an intelligent personal assistant for hearing impaired people

This work has been focused on the part of the population with hearing impairment who owns a dog and that worries about not listening the dog barks, specially when a risky situation is taking place at home. A survey was carried out on people with deafness problems to find out hazard situations which they are exposed at home. A system prototype was developed to be integrated as a component of ambient intelligence (AmI) for ambient assisted living (AAL) that serves to Hearing Impaired People (HIP). The prototype detects dog barks and notifies users through both a smart mobile app and a visual feedback. It consists of a connection between a Raspberry Pi 3 card and a ReSpeaker Mic Array v2.0 microphone array; a communication module with a smartphone was implemented, which displays written messages or vibrations when receiving notifications. The cylinder-shaped device was designed by the authors and sent it to 3D print with a resin material. The prototype recognized the barking efficiently by using a machine learning model based on Support Vector Machine technique. The prototype was tested with deaf people which were satisfied with precision, signal intensity, and activation of lights.

Acoustic camera system for measuring ultrasound communication in mice

Ultrasonic vocalizations in mice have recently been widely investigated as social behavior; however, using existing sound localization systems in home cages, which allow observations of more undisturbed behavior expressions, is challenging. We introduce a novel system, named USVCAM, that uses a phased microphone array and demonstrate novel vocal interactions under a resident-intruder paradigm. The extended applicability and usability of USVCAM may facilitate investigations of social behaviors and underlying physiological mechanisms.