Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-47180
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.
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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|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|
|Event End Date:||21 July 2010|
|Deposited On:||02 Mar 2011 15:16|
|Last Modified:||05 Apr 2016 14:51|
|Number of Pages:||0|
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