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: Sparse Spectrotemporal Coding of Sounds | EURASIP Journal on Applied Signal Processing 2003 7 659-667 2003 Hindawi Publishing Corporation Sparse Spectrotemporal Coding of Sounds David J. Klein Institute of Neuroinformatics University of Zurich and ETH Zurich Winterthurerstrasse 190 CH-8057 Zurich Switzerland Email djklein@ Peter Konig Institute of Neuroinformatics University of Zurich and ETH Zurich Winterthurerstrasse 190 CH-8057 Zurich Switzerland Email peterk@ Konrad P. Kording Institute of Neurology University College London Queen square London WC1N 3BG UK Email konrad@ Received 1 May 2002 and in revised form 28 January 2003 Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals. The analysis is typically well matched with the statistics of relevant natural sounds suggesting that it produces an optimal representation of the animal s acoustic biotope. We address this topic using simulated neurons that learn an optimal representation of a speech corpus. As input the neurons receive a spectrographic representation of sound produced by a peripheral auditory model. The output representation is deemed optimal when the responses of the neurons are maximally sparse. Following optimization the simulated neurons are similar to real neurons in many respects. Most notably a given neuron only analyzes the input over a localized region of time and frequency. In addition multiple subregions either excite or inhibit the neuron together producing selectivity to spectral and temporal modulation patterns. This suggests that the brain s solution is particularly well suited for coding natural sound therefore it may prove useful in the design of new computational methods for processing speech. Keywords and phrases sparse coding natural sounds spectrotemporal receptive fields spectral representation of speech. 1. INTRODUCTION The brain evolves both overindividual and .
