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Sound recognition with spiking silicon cochlea and Hidden Markov Models


Jaeckel, D; Moeckel, R; Liu, S C (2010). Sound recognition with spiking silicon cochlea and Hidden Markov Models. In: Ph.D. Research in Microelectronics and Electronics (PRIME), 2010 Conference, Berlin, 18 July 2010 - 21 July 2010.

Abstract

In this paper we explore the capabilities of a sound recognition system that combines both a novel bio-inspired custom silicon cochlea chip and a classical Hidden Markov Model (HMM). The cochlea chip front-end produces a form of representation that is analogous to the spike outputs of the biological cochlea. The system is trained with either of 2 target sounds (a clap or a bass drum) in the presence of different levels of white noise or colored noise. We provide experimental results that show 1) the system is able to detect a clap or a bass drum sound even if the amplitude of the target sound was not part of the training set and 2) the performance of the system in detecting a target sound in the presence of white noise or colored noise is around 90% for signal-to-noise ratios down to at least 0.8.

In this paper we explore the capabilities of a sound recognition system that combines both a novel bio-inspired custom silicon cochlea chip and a classical Hidden Markov Model (HMM). The cochlea chip front-end produces a form of representation that is analogous to the spike outputs of the biological cochlea. The system is trained with either of 2 target sounds (a clap or a bass drum) in the presence of different levels of white noise or colored noise. We provide experimental results that show 1) the system is able to detect a clap or a bass drum sound even if the amplitude of the target sound was not part of the training set and 2) the performance of the system in detecting a target sound in the presence of white noise or colored noise is around 90% for signal-to-noise ratios down to at least 0.8.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:21 July 2010
Deposited On:02 Mar 2011 15:16
Last Modified:05 Apr 2016 14:51
Number of Pages:0
ISBN:978-1-4244-7905-4
Permanent URL: http://doi.org/10.5167/uzh-47180

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