Direction of Arrival with One Microphone, a few LEGOs, and Non-Negative Matrix Factorization

Conventional approaches to sound source localization require at least two microphones. It is known, however, that people with unilateral hearing loss can also localize sounds. Monaural localization is possible thanks to the scattering by the head, though it hinges on learning the spectra of the various sources. We take inspiration from this human ability to propose algorithms for accurate sound source localization using a single microphone embedded in an arbitrary scattering structure. The structure modifies the frequency response of the microphone in a direction-dependent way giving each direction a signature. While knowing those signatures is sufficient to localize sources of white noise, localizing speech is much more challenging: it is an ill-posed inverse problem which we regularize by prior knowledge in the form of learned non-negative dictionaries. We demonstrate a monaural speech localization algorithm based on non-negative matrix factorization that does not depend on sophisticated, designed scatterers. In fact, we show experimental results with ad hoc scatterers made of LEGO bricks. Even with these rudimentary structures we can accurately localize arbitrary speakers; that is, we do not need to learn the dictionary for the particular speaker to be localized. Finally, we discuss multi-source localization and the related limitations of our approach.Comment: This article has been accepted for publication in IEEE/ACM Transactions on Audio, Speech, and Language processing (TASLP

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Biologically-inspired radar sensing

The natural world has an unquantifiable complexity and natural life exhibits remarkable techniques for responding to and interacting with the natural world. This thesis aims to find new approaches to radar systems by exploring the paradigm of biologically-inspired design to find effective ways of using the flexibility of modern radar systems. In particular, this thesis takes inspiration from the astonishing feats of human echolocators and the complex cognitive processes that underpin the human experience. Interdisciplinary research into human echolocator tongue clicks is presented before two biologically-inspired radar techniques are proposed, developed, and analyzed using simulations and experiments. The first radar technique uses the frequency-diversity of a radar system to localize targets in angle, and the second technique uses the degrees-of-freedom accessible to a mobile robotic platform to implement a cognitive radar architecture for obstacle avoidance and navigation

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

Acoustic tracking of whales’ underwater cruises is essential for protecting marine ecosystems. For cetacean conservationists, fewer hydrophones will provide more convenience in capturing high-mobility whale positions. Currently, it has been possible to use two hydrophones individually to accomplish direction finding or ranging. However, traditional methods only aim at estimating one of the spatial parameters and are susceptible to the detrimental effects of reverberation superimposition. To achieve complete whale tracking under reverberant interference, in this study, an intelligent acoustic tracking model (CIAT) is proposed, which allows both horizontal direction discrimination and distance/depth perception by mining unpredictable features of position information directly from the received signals of two hydrophones. Specifically, the horizontal direction is discriminated by an enhanced cross-spectral analysis to make full use of the exact frequency of received signals and eliminate the interference of non-source signals, and the distance/depth direction combines convolutional neural network (CNN) with transfer learning to address the adverse effects caused by unavoidable acoustic reflections and reverberation superposition. Experiments with real recordings show that 0.13 km/MAE is achieved within 8 km. Our work not only provides satisfactory prediction performance, but also effectively avoids the reverberation effect of long-distance signal propagation, opening up a new avenue for underwater target tracking

Acoustic localization of people in reverberant environments using deep learning techniques

La localización de las personas a partir de información acústica es cada vez más importante en aplicaciones del mundo real como la seguridad, la vigilancia y la interacción entre personas y robots. En muchos casos, es necesario localizar con precisión personas u objetos en función del sonido que generan, especialmente en entornos ruidosos y reverberantes en los que los métodos de localización tradicionales pueden fallar, o en escenarios en los que los métodos basados en análisis de vídeo no son factibles por no disponer de ese tipo de sensores o por la existencia de oclusiones relevantes. Por ejemplo, en seguridad y vigilancia, la capacidad de localizar con precisión una fuente de sonido puede ayudar a identificar posibles amenazas o intrusos. En entornos sanitarios, la localización acústica puede utilizarse para controlar los movimientos y actividades de los pacientes, especialmente los que tienen problemas de movilidad. En la interacción entre personas y robots, los robots equipados con capacidades de localización acústica pueden percibir y responder mejor a su entorno, lo que permite interacciones más naturales e intuitivas con los humanos. Por lo tanto, el desarrollo de sistemas de localización acústica precisos y robustos utilizando técnicas avanzadas como el aprendizaje profundo es de gran importancia práctica. Es por esto que en esta tesis doctoral se aborda dicho problema en tres líneas de investigación fundamentales: (i) El diseño de un sistema extremo a extremo (end-to-end) basado en redes neuronales capaz de mejorar las tasas de localización de sistemas ya existentes en el estado del arte. (ii) El diseño de un sistema capaz de localizar a uno o varios hablantes simultáneos en entornos con características y con geometrías de arrays de sensores diferentes sin necesidad de re-entrenar. (iii) El diseño de sistemas capaces de refinar los mapas de potencia acústica necesarios para localizar a las fuentes acústicas para conseguir una mejor localización posterior. A la hora de evaluar la consecución de dichos objetivos se han utilizado diversas bases de datos realistas con características diferentes, donde las personas involucradas en las escenas pueden actuar sin ningún tipo de restricción. Todos los sistemas propuestos han sido evaluados bajo las mismas condiciones consiguiendo superar en términos de error de localización a los sistemas actuales del estado del arte