Classroom speech intelligibility prediction using backpropagation Neural Network
Organized by Coimbatore Institute of Technology, 27th - 29th August 2007 at Coimbatore, Tamil Naidu, India.
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| Format: | Working Paper |
| Language: | en |
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Coimbatore Institute of Technology
2009
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| Online Access: | http://dspace.unimap.edu.my/123456789/6421 |
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| _version_ | 1831782165901213696 |
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| author | Paularaj, M. P. Sazali, Yaacob Ahmad Nazri Thagirarani, M. |
| author_facet | Paularaj, M. P. Sazali, Yaacob Ahmad Nazri Thagirarani, M. |
| author_sort | Paularaj, M. P. |
| building | UniMAP Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Perlis |
| content_source | UniMAP Library Digital Repository |
| continent | Asia |
| country | Malaysia |
| description | Organized by Coimbatore Institute of Technology, 27th - 29th August 2007 at Coimbatore, Tamil Naidu, India. |
| format | Working Paper |
| id | my.unimap-6421 |
| institution | Universiti Malaysia Perlis |
| language | en |
| publishDate | 2009 |
| publisher | Coimbatore Institute of Technology |
| record_format | dspace |
| spelling | my.unimap-64212009-08-18T07:34:10Z Classroom speech intelligibility prediction using backpropagation Neural Network Paularaj, M. P. Sazali, Yaacob Ahmad Nazri Thagirarani, M. Neural network Speech intelligibility Classroom acoustics Reverberation time Signal to noise ratio Neural networks (Computer science) Back propagation Organized by Coimbatore Institute of Technology, 27th - 29th August 2007 at Coimbatore, Tamil Naidu, India. In terms of individual communication, speech is the most important and efficient means, even in today's multi-media society. Thus, classrooms are mainly used for delivering speech between lecturers and students, it is important that acoustic designs accommodate and enhance such use. In achieving the highest possible speech intelligibility, the acoustical design of classrooms should be based on all the listeners in the classrooms. This paper investigates the effect of Signal to Noise Ratio (S/N) on Speech Transmission Index (STI) in University classrooms. The sound pressure levels are measured at different classrooms positions. Based on the measured speech levels, STI at various listeners' positions are determined and a simple backpropagation network model is developed to predict the STI at various listeners' positions and for various speech levels. 2009-07-10T03:18:05Z 2009-07-10T03:18:05Z 2007-08-27 Working Paper http://dspace.unimap.edu.my/123456789/6421 en International conference on Modeling and Simulation (CITICOMS 2007) Coimbatore Institute of Technology |
| spellingShingle | Neural network Speech intelligibility Classroom acoustics Reverberation time Signal to noise ratio Neural networks (Computer science) Back propagation Paularaj, M. P. Sazali, Yaacob Ahmad Nazri Thagirarani, M. Classroom speech intelligibility prediction using backpropagation Neural Network |
| title | Classroom speech intelligibility prediction using backpropagation Neural Network |
| title_full | Classroom speech intelligibility prediction using backpropagation Neural Network |
| title_fullStr | Classroom speech intelligibility prediction using backpropagation Neural Network |
| title_full_unstemmed | Classroom speech intelligibility prediction using backpropagation Neural Network |
| title_short | Classroom speech intelligibility prediction using backpropagation Neural Network |
| title_sort | classroom speech intelligibility prediction using backpropagation neural network |
| topic | Neural network Speech intelligibility Classroom acoustics Reverberation time Signal to noise ratio Neural networks (Computer science) Back propagation |
| url | http://dspace.unimap.edu.my/123456789/6421 |
| url_provider | http://dspace.unimap.edu.my/ |