A neural network approach to the automated detection of acoustic annoyance in hard disk drives /
"Annoyance" in this research is defined as the reduction of
| Main Author: | |
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| Format: | Thesis eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
[publisher not identified] ;
1994.
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| Subjects: | |
| Online Access: | Link to OAKTrust copy |
| Summary: | "Annoyance" in this research is defined as the reduction of analyzed and characterized using both time and frequency annoyance in hard disk drives. The aim is to be able to could not be found to predict the level of annoyance from detect excessive annoyance as a quality test while still in discussion is presented comparing the two methods and their domain analysis. Literature suggested that magnitude, time environment. This paper describes an artificial neural equipment also brings added noise to the environment. groups of annoying and not annoying. The Learning Vector In order to increase efficiency, computing equipment in the increase in efficiency is not optimized since the added network based approach for automating the detection of office environment is increasing rapidly. However, the productivity due to the acoustic disturbance in the office quantitative domain. First, the hard disk drive sounds were Quantization and the Back Propagation algorithms were both research is how to map subjective information to a the effect of annoyance; however, a deterministic method the manufacturing environment. The true question of this this information. The hard drives were divided into two training results. used to train networks to perform this decision function. A variance, and frequency combinations were all contributors to |
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| Item Description: | "Major subject: Electrical Engineering". Vita. |
| Physical Description: | x, 50 leaves : illustrations ; 28 cm. Also available online. |
| Bibliography: | Includes bibliographical references. |