Noise Reduction Filter of Cascaded Sandglass-type Neural Network Adaptively Controlled by Noise Intensity for Speech Signal

Autor:	Toshie Namiki, Naoki Isu, Yuki Furumoto, Kazuhiro Sugata, Hiroki Yoshimura, Tadaaki Simizu
Rok vydání:	2003
Předmět:	Hearing aid Engineering Artificial neural network Noise measurement business.industry Noise reduction Speech recognition medicine.medical_treatment Noise intensity Intelligibility (communication) medicine Effective input noise temperature Electrical and Electronic Engineering business
Zdroj:	IEEJ Transactions on Electronics, Information and Systems. 123:430-439
ISSN:	1348-8155 0385-4221
DOI:	10.1541/ieejeiss.123.430
Popis:	An adaptive noise reduction filter composed of Cascaded Sandglass-type Neural Network (CSNNRF) is proposed to develop a hearing aid appliance. The number of unit sandglass-type neural networks (SNNs) is controlled adaptively by noise intensity. Usually the hearing aid works outside where noise intensity is altering all the times. An adaptive noise reduction filter controlled by noise intensity is essential to cope with these circumstances. Each SNN has a three-layer structure and consists of the same number of neural units in the input and output layers and a single neural unit in the hidden layer.SNN are connected in cascaded to be CSNNRF. The number of unit SNNs is adaptively determined by our algorithms so that hearing intelligibity for speech signal may be processed more preferablely. To determine the number of SNNs, we regard significance on the improvement of intelligibility more than numerical value itself such as S/N ratio of speech signal.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c946168f218256632e9631fb3ac4ef71 https://doi.org/10.1541/ieejeiss.123.430 Zobrazit plný text záznamu