Life Sounds Extraction and Classification in Noisy Environment

International audienceThis paper deals with the sound event detection in a noisy environment and presents a first classification approach. Detection is the first step of our sound analysis system and is necessary to extract the significant sounds before ini-tiating the classification step. We present three original event detection algorithms. Among these algorithms, one is based on the wavelet and gives the best performances. We evaluate and compare their performance in a noisy en-vironment with the state of the art algorithms in the field. Then, we present a statistical study to obtain the acous-tical parameters necessary for the training and, the sound classification results. The detection algorithms and sound classification are applied to medical telemonitoring. We re-place video camera by microphones surveying life sounds in order to preserve patient's privacy

Sound-Event Recognition with a Companion Humanoid

International audienceIn this paper we address the problem of recognizing everyday sound events in indoor environments with a consumer robot. Sounds are represented in the spectro-temporal domain using the stabilized auditory image (SAI) representation. The SAI is well suited for representing pulse-resonance sounds and has the interesting property of mapping a time-varying signal into a fixed-dimension feature vector space. This allows us to map the sound recognition problem into a supervised classification problem and to adopt a variety of classifications schemes. We present a complete system that takes as input a continuous signal, splits it into significant isolated sounds and noise, and classifies the isolated sounds using a catalogue of learned sound-event classes. The method is validated with a large set of audio data recorded with a humanoid robot in a house. Extended experiments show that the proposed method achieves state-of-the-art recognition scores with a twelve-class problem, while requiring extremely limited memory space and moderate computing power. A first real-time embedded implementation in a consumer robot show its ability to work in real conditions

Estimation of reverberation time from binaural signals without using controlled excitation

Tässä työssä tutkittiin jälkikaiunta-ajan estimointia binauraalisesta äänisignaalista. Jälkikaiunta-aika (RT) on yksi tärkeimmistä akustisista parametreista, jonka tuntemisesta olisi hyötyä useissa sovelluksissa, kuten laajennetussa äänitodellisuudessa, matkaviestinnässä ja älykkäissä kuulolaitteissa. Tämän tyyppisissä sovelluksissa estimaattia jälkikaiunta-ajasta ei yleensä ole saatavilla eikä sitä ole mahdollista mitata standardimenetelmillä. Jälkikaiunta-ajan estimointia varten kehitettiin automaattinen menetelmä, joka ei vaadi mitään etukäteistietoa ympäröivästä akustisesta tilasta ja toimii mielivaltaisella binauraalisella signaalilla, toisin kuin perinteiset mittausmenetelmät. Algoritmin perusideana on ensin paikantaa jälkikaiunta-analysiin sopivat signaalin osat ja sen jälkeen laskea jälkikaiunta perustuen Schröderin käänteiseen integrointimenetelmään. Jälkikaiunta-aikaestimaatti saadaan lopulta tilastollisen analyysin tuloksena. Binauraalisuutta hyödynnetään käyttämällä kanavien välistä koherenssifunktiota analyysissä. Käänteiseen integrointiin ja sitä seuraavaan suoran sovitukseen liittyvien rajojen etsintään keksittiin muutamia uusia metodeja. Algoritmista totetuttiin reaaliaikaversio C++ -kielellä ja algoritmin toimintaa arvioitiin sekä synteettisillä että todellisilla nauhoitetuilla signaaleilla. Tulokset osoittavat, että algoritmi kykenee estimoimaan jälkikaiunta-ajan melko tarkasti useimmissa tapauksissa, vaikka eri akustisten tilojen välillä onkin vaihtelua.This thesis concentrates on the task of estimating reverberation time from binaural audio signals. The reverberation time (RT) is one of the most important acoustic parameters describing the acoustic behavior of a space. An estimate of this parameter would be advantageous to many audio applications, such as augmented reality audio, mobile communications and intelligent hearing aids. Usually in these kind of applications no estimates of the room acoustic parameters are available and it is not possible to acquire the parameters online using standard measurement techniques. An automatic algorithm for estimating the reverberation time was developed. This algorithm requires no a priori knowledge of the surrounding space and operates on an arbitrary binaural input signal, as opposed to standard acoustic measurement techniques. The basic idea of the algorithm is to first locate suitable signal segments for subsequent analysis and then calculate the reverberation time by applying the standard Schroeder integration method to each segment followed by some statistical analysis to derive a final RT estimate. The binaural nature of the input signals is also taken advantage of by using the inter-channel coherence in the analysis. Some new ideas for finding the integration and line fitting limits were also developed. A real-time version of the algorithm was implemented in C++. The algorithm performance was evaluated with both synthetic signals and real recordings. The results show that the algorithm can determine the reverberation quite accurately in most cases, even though there is some degree of variability between different rooms

Detección e identificación de señales sonoras en entornos asistivos.

150 p.El trabajo desarrollado en este documento de Tesis Doctoral tiene como principal objetivo el estudio y aplicabilidad de técnicas de reconocimiento de sonidos no relacionados con el habla, tales como timbres de puerta, grifos abiertos, despertadores, etc., que ayuden a mejorar la independencia y calidad de vida de las personas con discapacidad auditiva.En esta investigación se han desarrollado sistemas de reconocimiento capaces de trabajar en tiempo real utilizando micrófonos profesionales con una localización fija. Estos sistemas han sido diseñados tanto para avisar a las personas con problemas auditivos de sonidos de interés como para su uso en sistemas inteligentes que utilicen esta información para el reconocimiento de actividades de la vida diaria de la persona. No obstante, la principal contribución de esta tesis reside en la investigación de este tipo de sistemas en teléfonos móviles donde las prestaciones hardware están más limitadas y las condiciones de entrenamiento de los sonidos y las de validación o testeo varían. Se ha demostrado cómo optimizando los algoritmos de detección y clasificación, estos sistemas pueden ser funcionales en dispositivos móviles en tiempo real. El trabajo en este campo ha derivado en el desarrollo de una aplicación funcional para teléfonos móviles, capaz de funcionar en tiempo real y diseñada en base a pautas de accesibilidad para el apoyo de personas con discapacidad auditiva

Life Sounds Extraction and Classification in Noisy Environment

This paper deals with the sound event detection in a noisy environment and presents a first classification approach. Detection is the first step of our sound analysis system and is necessary to extract the significant sounds before initiating the classification step. We present three original event detection algorithms. Among these algorithms, one is based on the wavelet and gives the best performances. We evaluate and compare their performance in a noisy environment with the state of the art algorithms in the field. Then, we present a statistical study to obtain the acoustical parameters necessary for the training and, the sound classification results. The detection algorithms and sound classification are applied to medical telemonitoring. We replace video camera by microphones surveying life sounds in order to preserve patient's privacy