Localisation sonore | EPFL Graph Search

vignette|Localisation sonore avec des indices binauraux (une oreille capte plus de son que l'autre). La localisation sonore désigne la capacité du système auditif à déterminer la position spatiale d'une source sonore au moyen de différents indices physiques. Ces indices peuvent être classés en deux catégories : les indices binauraux (c'est-à-dire issus de l'analyse combinée des sons parvenant aux deux oreilles) et des indices monauraux (c'est-à-dire ceux que l'on peut déterminer avec une seule oreille). Chez l'être humain, le principal indice utilisé pour la localisation sonore est la comparaison interaurale : la différence de niveau et de délai. L'ingénierie acoustique exploite ce phénomène pour reproduire une impression d'espace sonore, par exemple via la stéréophonie ou les systèmes de enceintes home cinema. Un autre indice est le HRTF, désignant la transformation du son par la tête, le cerveau étant capable d'interpréter la provenance d'un son par sa coloration. Les prédateurs no

Pyramic array: An FPGA based platform for multi-channel audio acquisition

Juan Azcarreta Ortiz

Microphone arrays techniques present compelling applications for robotic applications. Those techniques can allow robots to listen to their environment and infer clues from it. Such features might enable capabilities such as natural interaction with humans, interpreting spoken commands or the localization of victims during search and rescue tasks. However, under noisy conditions robotic implementations of microphone arrays might degrade their precision when localizing sound sources. For practical applications, human hearing still leaves behind microphone arrays. Daniel Kisch is an example of how humans are able to efficiently perform echo-localization to recognize their environment, even in noisy and reverberant environments. For ubiquitous computing, another limitation of acoustic localization algorithms is within their capabilities of performing real-time Digital Signal Processing (DSP) operations. To tackle those problems, tradeoffs between size, weight, cost and power consumption compromise the design of acoustic sensors for practical applications. This works presents the design and operation of a large microphone array for DSP applications in realistic environments. To address those problems this project introduces the Pyramic sound capture system designed at LAP in EPFL. Pyramic is a custom hardware which possesses 48 microphones distributed in the edges of a tetrahedron. The microphone arrays interact with a Terasic DE1-SoC board from Altera Cyclone V family devices, which combines a Hard Processor System (HPS) and a Field Programmable Gate Array (FPGA) in the same die. The HPS part integrates a dual-core ARM-based Cortex-A9 processor, which combined with the power of FPGA design suitable for processing multichannel microphone signals. This thesis explains the implementation of the Pyramic array. Moreover, FPGA-based hardware accelerators have been designed to implement a Master SPI communication with the array and a parallel 48 channels FIR filters cascade of the audio data for delay-and-sum beamforming applications. Additionally, the configuration of the HPS part allows the Pyramic array to be controlled through a Linux based OS. The main purpose of the project is to develop a flexible platform in which real-time echo-location algorithms can be implemented. The effectiveness of the Pyramic array design is illustrated by testing the recorded data with offline direction of arrival algorithms developed at LCAV in EPFL.

2016

Binaural localization and separation techniques

Harald Viste

Abstract Based on binaural signals, i.e. the signals observed at the two ears, a listener can localize and recognize different sound sources and then focus on one of these. For decades, researchers have tried to invent a machine that can do the same under similar conditions. Despite all the efforts, the human auditory system is, by far, superior to any machine that has been devised. The topic of this thesis is computational techniques for the localization and separation of sources in binaural signals. In order to give an overview of different areas of research that have considered the problems of source localization and separation, we start with a review of existing techniques. This provides the background for the techniques that we propose subsequently. Binaural Localization The most important cues for localization of sound sources in binaural signals are the level and time differences between the ears. We propose a technique for the joint evaluation of these cues where noisy level difference estimates are combined with less noisy but ambiguous time difference estimates in order to provide accurate azimuth estimates. The proposed technique enables the localization of sources and the tracking of these in dynamic scenes. Head model Based on a study of the level and time differences as function of azimuth angle for different heads, we propose a generic model that is parametrized by the distance between the ears only. This enables the use of the binaural localization technique mentioned above for a listener whose head related transfer functions have not been measured. Binaural separation For the separation of sources we propose a method based on spatial windowing in the azimuth parameter space. Separation of overlapping partials Finally, we propose a technique for the separation of overlapping partials in mixtures of harmonic instruments. The technique is based on the similarity of temporal envelopes between the different partials of a harmonic note.

EPFL2004

Beamforming of a microphones array

Kévin Marc Jean Decoster

Beamforming is the technique that permits to attenuate or increase the sound coming in a particular direction, it is in other word a way to estimate the power coming from a particular direction. It is therefore also used as direction of arrival estimator (DOA), and thus, signal source localizing. For this project, I have tried to come up with a robust and efficient design that allows DOA estimation of source signals and attenuates the noise coming from any others directions than the steering vector in real time using MATLAB.

2015