An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles

This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, whi...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2011-08, Vol.130 (2), p.893-903
Main Authors: Johansson, A.Torbjörn, White, Paul R.
Format: Article
Language:English
Subjects:
Acoustical measurements and instrumentation
Acoustics
Adaptive filters
Algorithms
Animals
Birds
Cetacea - physiology
Compensation
Environmental Monitoring - methods
Exact sciences and technology
Filtering
Fourier Analysis
Fundamental areas of phenomenology (including applications)
Human
Humans
Marine
Marine mammals
Pattern Recognition, Automated
Physics
Reproducibility of Results
Signal Processing, Computer-Assisted
Sound Spectrography
Time Factors
Tracking
Vocalization, Animal
Voice
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances.
ISSN:0001-4966
1520-8524
DOI:10.1121/1.3609117