The shift-ACF: Detecting multiply repeated signal components

We propose a novel method for detecting multiply repeated signal components within a source signal. Whereas standard methods such as classical autocorrelation are usually tailored to detecting signal components that are repeated once, our approach, the so called shift-autocorrelation, can be used to...

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Bibliographic Details
Main Author: Kurth, Frank
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
Conferences
Correlation
Repeated components
shift-autocorrelation
Spectrogram
Trajectory
Vectors
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Description
Summary:We propose a novel method for detecting multiply repeated signal components within a source signal. Whereas standard methods such as classical autocorrelation are usually tailored to detecting signal components that are repeated once, our approach, the so called shift-autocorrelation, can be used to improve detection performance by explicitly assuming multiply repeated components. In this paper we introduce the shift-autocorrelation formally and give theoretical evidence of its performance compared to classical autocorrelation. By defining tempograms based on shift-autocorrelation, we apply the method to the detection of underwater mammal sounds, showing its superiority to classical approaches in a practical evaluation.
ISSN:1931-1168
1947-1629
DOI:10.1109/WASPAA.2013.6701820