Sound Classification in a Smart Room Environment: an Approach using GMM and HMM Methods

International audienceBecause of cost or convenience reasons, patients or elderly people would be hospitalized at home and smart information systems would be needed in order to assist human operators. In this case, position and physiologic sensors give already numerous informations, but there are few studies for sound use in patient's habitation. However, sound classification and speech recognition may greatly increase the versatility of such a system: this will be provided by detecting short sentences or words which could characterize a distress situation for the patient. Analysis and classification of sounds emitted in patient's habitation may be useful for patient's activity monitoring. GMMs and HMMs are well suited for sound classification. Until now, GMMs are frequently used for sound classification in smart rooms because of their low computational costs, but HMMs should allow a finer analysis: indeed the use of 3 states HMMs should allow better performances by taking into account the variation of the signal according to time. For this framework a new sound corpus was recorded in experimental conditions. This corpus includes 8 sound classes useful for our application. The choice of needed acoustical features and the two approaches are presented. Then an evaluation is made with the initial corpus and with additional experimental noise. The obtained results are compared. At the end of this framework a segmentation module is presented. This module has the ability of extracting isolated sounds in a record by the means of a wavelet filtering method which allows the extraction in noisy conditions

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 Detection and Classification for Medical Telesurvey

International audienceMedical Telesurvey needs human operator assistance by smart information systems. 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 sig-nificant sounds before initiating the classification step. An algorithm based on the Wavelet Transform is evaluated in noisy environment. Then Wavelet based cepstral coeffi-cients are proposed and their results are compared with more classical parameters. Detection algorithm and sound classification methods are applied to medical telemonitor-ing. In our opinion, microphones surveying life sounds are better preserving patient privacy than video cameras

COMMUNICATION BETWEEN A MULTICHANNEL AUDIO ACQUISITION AND AN INFORMATION SYSTEM IN A HEALTH SMART HOME FOR DATA FUSION

International audienceThe Health Integrated Smart Home Information System (HIS²) has been developed in the TIMC laboratory for the remote monitoring of the health status of an elderly person during daily life at home. This aims at improving patients' life conditions and at reducing the costs of the long hospitalization. The design of this system is based on a CAN network linked to volumetric, physiological and environment sensors. In addition, a collaboration between the TIMC and the CLIPS laboratories permitted to replace the video camera, not well accepted by the patients by a system based on a multichannel Sound Acquisition. The coupling between both systems will enable to detect if the person is in a situation of distress or not. Both systems locally processe in real time the incoming data and communicate using a CAN network to display the health status. This article describes the system architecture of both systems, practical solutions for their communication and the evaluation results

Embedded Implementation of Distress Situation Identification through Sound Analysis

International audienceThe safety of elderly people living alone at home might be a crucial problem because of the growing aging population and the high risk of home accidents such as falls. Medical remote monitoring systems may increase the safety of such people in detecting and quickly announcing the state of emergency. We have already proposed a sound medical remote monitoring system. Distress sounds like glass breaking, screams, falls and distress expressions like "Help", "A doctor, please!" are detected and recognized through a continuous analysis of the sound flow. In the case of distress situation identification, the software can send an alarm with the recognized data to a close person and/or to a medical center. In this paper, a real-time implementation of this system is presented. The advantages of this implementation on an Embedded PC, equipped with a classical sound card and a microphone, are the reduced dimensions, the silence (fan less) and the cost. In the same time, this implementation is flexible and can be installed also on desktop or laptop PC

Speech and Sound Analysis : an Application of Probabilistic Models

International audienceno abstrac

Generic Implementation of a Distress Sound Extraction System for Elder Care

Medical remote monitoring at home is an alternative to improve the patient’s comfort, to detect distress situation rapidly and reduce hospitalization costs. Physiologic and position sensors give already numerous information, but sound classification can give interesting additional information. A Real-Time implementation of a smart sound system capable of detecting and identifying sound events in noisy conditions is presented in this paper. The advantage of this implementation is the use of a generic PC station: the hardware requirements are only a sound card, a microphone and an internet link used to transmit alarm. In the case of an alarm, the information can be sent through network to a remote monitoring center and/or to a close person by email or SMS. The system is composed of 2 modules: detection and classification. The event detection module is carried out in real time in order to extract possible alarm sounds. The sound classification module is runs in a parallel task; it carries out a first segmentation between sound and speech. In the speech case, a speech recognition system is launched (not described in this paper) and in the sound case, a I

EMACOP : Environnement Multimédia pour l'Acquisition et la gestion de COrpus Parole

International audienceThis paper presents an Multimedia Environment for Acquiring and Managing Speech Corpora, running under Windows 95 and windows NT. It provides graphical interface functions to process the signal in order to compute acoustical parameters used in speech analysis, recognition and synthesis. Entries' extraction from main database is also provided from other sources. The new way of programming through the Microsoft COM (Component Object Model), which gives encapsulation/aggregation mechanisms and independence for each module, is used to increase reliability, flexibility and maintainability

Sound Detection And Classification Through Transient Models Using Wavelet Coefficient Trees

Medical Telesurvey needs human operator assistance by smart information systems. Usual sound classification may be applied to medical monitoring by use of microphones in patient's habitation. Detection is the first step of our sound analysis system and is necessary to extract the significant sounds before initiating the classification step. This paper proposes a detection method using transient models, based upon dyadic trees of wavelet coefficients to insure short detection delay. The classification stage uses a Gaussian Mixture Model classifier with classical acoustical parameters like MFCC. Detection and classification stages are evaluated in experimental recorded noise condition which is nonstationary and more aggressive than simulated white noise and fits with our application. Wavelet filtering methods are proposed to enhance performances in low signal to noise ratios