### Abstract

Acoustic transients - short, impulsive bursts of acoustic energy - are a rich source of information in the natural world. Biological systems process them quickly and economically. In this article, we describe a biologically inspired analog very-large-scale integration (VLSI) architecture for real-time classification of acoustic transients. Judicious normalization of time-frequency signals allows an elegant and robust implementation of a correlation algorithm. The algorithm replaces analog-analog multiplication with binary multiplexing of analog signals. This removes the need for analog storage and analog multiplication. Simulations show that the resulting algorithm has the same out-of-sample classification performance (about 93% correct) as a template-matching algorithm based on conventional analog correlation. This development paves the way for intelligent acoustic processing in low-power applications such as cellular telephones and debit cards.

Original language | English (US) |
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Pages (from-to) | 244-252 |

Number of pages | 9 |

Journal | Johns Hopkins APL Technical Digest (Applied Physics Laboratory) |

Volume | 18 |

Issue number | 2 |

State | Published - Apr 1 1997 |

Externally published | Yes |

### Keywords

- Acoustic transients
- Analog VLSI
- Matched filters
- Neural computation
- Speech recognition

### ASJC Scopus subject areas

- Engineering(all)
- Physics and Astronomy(all)

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## Cite this

*Johns Hopkins APL Technical Digest (Applied Physics Laboratory)*,

*18*(2), 244-252.