Automatic Speech Processing Methods For Bioacoustic Signal Analysis: A Case Study Of Cross-disciplinary Acoustic Research

Abstract

Automatic speech processing research has produced many advances in the analysis of time series. Knowledge of the production and perception of speech has guided the design of many useful algorithms, and automatic speech recognition has been at the forefront of the machine learning paradigm. In contrast to the advances made in automatic speech processing, analysis of other bioacoustic signals, such as those from dolphins and bats, has lagged behind. In this paper, we demonstrate how techniques from automatic speech processing can significantly impact bioacoustic analysis, using echolocating bats as our model animal. Compared to conventional techniques, machine learning methods reduced detection and species classification error rates by an order of magnitude. Furthermore, the signal-to-noise ratio of an audible monitoring signal was improved by 12 dB using techniques from noise-robust feature extraction and speech synthesis. The work demonstrates the impact that speech research can have across disciplines. © 2006 IEEE.

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

978-142440469-8

International Standard Serial Number (ISSN)

1520-6149

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Dec 2006

This document is currently not available here.

Share

 
COinS