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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Source Separation with One Ear: Proposition for an Anthropomorphic Approach | EURASIP Journal on Applied Signal Processing 2005 9 1365-1373 2005 Hindawi Publishing Corporation Source Separation with One Ear Proposition for an Anthropomorphic Approach Jean Rouat Departement de Genie Electrique et de Genie Informatique Universite Sherbrooke 2500 boulevard de l Universite Sherbrooke QC Canada J1K2R1 Equipe de Recherche en Micro-electronique et Traitement Informatique des Signaux ETMETIS Departement de Sciences Appliques Universite du Quebec a Chicoutimi 555 boulevard de l Universite Chicoutimi Québec Canada G7H 2B1 Email jean.rouat@ieee.org Ramin Pichevar Departement de Genie Electrique et de Genie Informatique Universite Sherbrooke 2500 boulevard de l Universite Sherbrooke QC Canada J1K2R1 Email ramin.pichevar@usherbrooke.ca Equipe de Recherche en Micro-electronique et Traitement Informatique des Signaux ETMETIS Departement de Sciences Appliques Universite du Quebec a Chicoutimi 555 boulevard de l Universite Chicoutimi Québec Canada G7H 2B1 Received 9 December 2003 Revised 23 August 2004 We present an example of an anthropomorphic approach in which auditory-based cues are combined with temporal correlation to implement a source separation system. The auditory features are based on spectral amplitude modulation and energy information obtained through 256 cochlear filters. Segmentation and binding of auditory objects are performed with a two-layered spiking neural network. The first layer performs the segmentation of the auditory images into objects while the second layer binds the auditory objects belonging to the same source. The binding is further used to generate a mask binary gain to suppress the undesired sources from the original signal. Results are presented for a double-voiced 2 speakers speech segment and for sentences corrupted with different noise sources. Comparative results are also given using PESQ perceptual evaluation of speech quality scores. The spiking neural network is fully adaptive and unsupervised. Keywords and phrases .
