Optimizing correlation algorithms for hardware-based transient classification

R. Timothy Edwards, Gert Cauwenberghs, Fernando J Pineda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The performance of dedicated VLSI neural processing hardware depends critically on the design of the implemented algorithms. We have previously proposed an algorithm for acoustic transient classification [1]. Having implemented and demonstrated this algorithm in a mixed-mode architecture, we now investigate variants on the algorithm, using time and frequency channel differencing, input and output normalization, and schemes to binarize and train the template values, with the goal of achieving optimal classification performance for the chosen hardware.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Pages678-684
Number of pages7
ISBN (Print)0262112450, 9780262112451
Publication statusPublished - 1999
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: Nov 30 1998Dec 5 1998

Other

Other12th Annual Conference on Neural Information Processing Systems, NIPS 1998
CountryUnited States
CityDenver, CO
Period11/30/9812/5/98

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Edwards, R. T., Cauwenberghs, G., & Pineda, F. J. (1999). Optimizing correlation algorithms for hardware-based transient classification. In Advances in Neural Information Processing Systems (pp. 678-684). Neural information processing systems foundation.