Introduction to artificial neural systems
Introduction to artificial neural systems
Segmenting time series with a hybrid neural networks - hidden Markov model
Eighteenth national conference on Artificial intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Neural Computation
Information extraction from sound for medical telemonitoring
IEEE Transactions on Information Technology in Biomedicine
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Nonverbal acoustic communication in human-computer interaction
Artificial Intelligence Review
Listen-to-nose: a low-cost system to record nasal symptoms in daily life
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hiddenMarkovmodel (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybridmodel improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated.