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Multi-channel Classification of Respiratory Sounds
2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006In this study, respiratory sounds of pathological and healthy subjects were analyzed via frequency spectrum and AR model parameters with a view to construct a diagnostic aid based on auscultation. Each subject is represented by 14 channels of respiratory sound data of a single respiration cycle.
Cemile Asli Yilmaz, Yasemin P. Kahya
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Classification of Complex Sounds
1991Abstract : The work accomplished during the period, 11/1/90 - 10/30/91, involves the use of COSS analysis to estimate weights in various profile analysis tasks. This technique is a method for investigating how spectral information is used by listeners to discriminate complex sounds.
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Cluster analysis and classification of heart sounds
Biomedical Signal Processing and Control, 2009Acoustic heart signals, generated by the mechanical processes of the cardiac cycle, carry significant information about the underlying functioning of the cardiovascular system. We describe a computational analysis framework for identifying distinct morphologies of heart sounds and classifying them into physiological states.
Guy Amit, Noam Gavriely, Nathan Intrator
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Automated classification of swallowing and breadth sounds
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005The goal of this study was to develop an automated and objective method to separate swallowing sounds from breath sounds. Swallowing sound detection can be utilized as part of a system for swallowing mechanism assessment and diagnosis of swallowing dysfunction (dysphagia) by acoustical means. In this study, an algorithm based on multilayer feed forward
Mohammad, Aboofazeli, Zahra, Moussavi
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Agile Edge Classification of Ocean Sounds
2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020The maritime environment is characterized by a scarcity of resources of power, sensing, processing, and communications. The resource constraints impose limitations in information acquisition which involves data collection and data processing to yield meaningful statistics. The contribution of this work is on custom software and hardware methods for low
Stelios Neophytou +6 more
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Unsupervised classification of heart sound recordings
2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013An unsupervised framework for classifying heart sound data is proposed in this paper. Our goal is to cluster unknown heart sound recordings, such that each cluster contains sound recordings belonging to the same heart diseases or normal heart beat category.
Wei-Ho Tsai, Sung-How Su, Cin-Hao Ma
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Feature selection for swallowing sounds classification
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007In recent years swallowing sounds analysis have received great attention for observing the abnormalities in swallowing mechanisms. In this paper a comprehensive set of features were extracted from time and frequency domains characteristics of the signals.
Azadeh, Yadollahi, Zahra, Moussavi
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Analysis of the perception and classification of synthetic sounds
Computers and Biomedical Research, 1979Abstract Sparse acoustic stimuli (SAS), with speech-like features, measure cortical auditory processing. Perceptions of SAS by normal subjects and patients can be arranged in multidimensional contingency tables and tested for departure from homogeneity.
J W, Drane, D M, Daly, D D, Daly
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Computerized classification of temporomandibular joint sounds
IEEE Transactions on Biomedical Engineering, 2000Sounds, such as clicking and/or crepitation, evoked in the temporomandibular (jaw) joint during function may indicate pathology. Analysis of the reduced interference time-frequency distribution of these sounds is of diagnostic value. However, visual evaluation is expensive and error prone, and there is, thus, a need for automated analysis.
Dragan Djurdjanovic +4 more
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The Journal of the Acoustical Society of America, 2007
A system and method to identify a sound source among a group of sound sources. The invention matches the acoustic input to a number of signal models, one per source class, and produces a goodness-of-match number for each signal model. The sound source is declared to be of the same class as that of the signal model with the best goodness-of-match if ...
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A system and method to identify a sound source among a group of sound sources. The invention matches the acoustic input to a number of signal models, one per source class, and produces a goodness-of-match number for each signal model. The sound source is declared to be of the same class as that of the signal model with the best goodness-of-match if ...
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