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A Neural Network Trained Microphone Array System for Noise Reduction
Responsible organisation
1996 (English)Conference paper, (Refereed) Published
Abstract [en]

This paper presents a neural network based microphone array system, which is capable to continuously perform speech enhancement and adaptation to nonuniform quantization, such as A-law and $mu@-law. Such a quantizer is designed to increase the Signal to Quantization Noise Ratio (SQNR) for small amplitudes in telecommunications systems. The proposed method primarily developed for hands-free mobile telephones, suppresses the ambient car noise with approximately 10 dB. The system is based upon a multi-layered nonlinear back-propagation trained network by using a built-in calibration technique.

Place, publisher, year, edition, pages
Kyoto, Japan: IEEE , 1996.
Keyword [en]
Microphones, Interference suppression, Speech analysis, Signal to noise ratio, Cellular telephone systems, Backpropagation, Algorithms
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-9725ISI: A1996BG22Q00032Local ID: oai:bth.se:forskinfo9CE1C1F7594AC6C4C12568A3002CAA3DOAI: oai:DiVA.org:bth-9725DiVA: diva2:837652
Conference
1996 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing (NNSP96)
Available from: 2012-09-18 Created: 2000-03-15 Last updated: 2015-06-30Bibliographically approved

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  • apa
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