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Journal of the Association for Information Science and Technology, Volume 76, Issue 4, Page 649-664, April 2025.Abstract Many theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues four important HIB theories are insufficient for describing users' search strategies for data because of assumptions about the attributes of objects that users seek.
Anthony J. Million +3 more
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The classification of ringing sounds
International Conference on Acoustics, Speech, and Signal Processing, 2002The problem of ringing sound (e.g. telephone, alarm clock, etc.) classification is addressed. A time-domain model, exhibiting the phenomenon of interference is found to have behavior similar to that exhibited by a telephone ringing sound. This motivates a frequency-domain production model which asserts that ringing sounds can be modeled as bandpass ...
Michael J. Paradie, S. Hamid Nawab
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Domestic Sound Classification with Deep Learning
2023 IEEE Sensors Applications Symposium (SAS), 2023Smart sensing and AI technologies are playing an increasingly important role in supporting aging in place, including embedded microphone devices that can contribute to safety by recognizing home events that need attention. In this work, we study sound event detection through classifying different sources of sounds.
Zhenyu Zhang +7 more
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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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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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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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