Dual-microphone Sounds of Daily Life classification for telemonitoring in a noisy environment
ABSTRACT Telemonitoring of elderly people in their homes using video cameras is complicated by privacy concerns, and hence sound has emerged as a promising alternative that is more acceptable to users. We investigate methods to address the accuracy degradation of sound classification that arises in the presence of background noise typical of a practical telemonitoring situation. A dual microphone configuration is used to record a database of Sounds of Daily Life (SDL) in a kitchen. We introduce a new algorithm employing the eigenvalues of the cross-spectral matrix of the recorded signals to detect the endpoints of a SDL in the presence of background noise. Independent component analysis is also used to improve the signal to noise ratio of the SDL. Results on a 7-class noisy SDL classification problem show that the error rate the proposed SDL classification system can be improved by up to 97% relative to a single-microphone system without noise reduction techniques, when evaluated on a large SDL database with SNRs in the range 0–28 dB.
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ABSTRACT: Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patient's comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a data fusion process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance.IEEE Transactions on Information Technology in Biomedicine 05/2006; 10(2):264-74. · 1.98 Impact Factor
Conference Proceeding: Language Identification using Warping and the Shifted Delta Cepstrum[show abstract] [hide abstract]
ABSTRACT: This paper proposes the novel use of feature warping for automatic language identification, in combination with the shifted delta cepstrum (SDC) and perceptual linear predictive coefficients in a Gaussian mixture model (GMM) based system. Experimental results on various configurations of front-end techniques reported herein demonstrate that, besides providing robustness against channel mismatch and noise as found in existing literature, feature warping is useful more generally as a technique for pre-mapping data for improved compatibility with a GMM back-end. The configuration reported in this paper provides a language identification performance of 76.4% using the OGI/NIST database, a 46.5% relative reduction in error rate when compared with a benchmark system employing Mel frequency cepstral coefficients and the SDCMultimedia Signal Processing, 2005 IEEE 7th Workshop on; 12/2005
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ABSTRACT: In this study the performance of a noise reduction strategy applied to cochlear implants is evaluated. The noise reduction strategy is based on a 2-channel adaptive filtering strategy using two microphones in a single behind-the-ear hearing aid. Four adult LAURA cochlear implant users (Peeters et al., 1993) took part in the experiments. The tests included identification of monosyllabic CVC (consonant-vowel-consonant) words and measurements of the speech reception threshold (SRT) of lists of numbers, in background noise presented at 90 degrees relative to the 0 degrees frontal direction of the speech. Percent correct phoneme scores for the CVC words at signal to noise ratios (SNRs) of -5, 0, and +5 dB in steady speech-weighted noise at 60 dB SPL and SRTs for numbers in speech-weighted steady and nonsteady ICRA noise were both obtained in conditions with and without the noise reduction pre-processing. Physical SNR improvements of the noise reduction system are evaluated as well, as a function of the direction of the noise source. Highly significant improvements in speech understanding, corresponding on average to an SNR improvement of about 10 dB, were observed with this 2-channel adaptive filtering noise reduction strategy using both types of speech-noise test materials. These perceptual evaluations agree with physical evaluations and simulations of this noise reduction strategy. Taken together, these data demonstrate that cochlear implantees may increase their speech intelligibility in noisy environments with the use of multimicrophone noise reduction systems.Ear and Hearing 11/2001; 22(5):420-30. · 3.26 Impact Factor