Authors:
B. Dugnol
1
;
C. Fernández
1
;
G. Galiano
2
and
J. Velasco
1
Affiliations:
1
Universidad de Oviedo, Spain
;
2
Universidad De Oviedo, Spain
Keyword(s):
Parametric model, Chirplet transform, Instantaneous frequency, Signal separation.
Related
Ontology
Subjects/Areas/Topics:
Acoustic Signal Processing
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Speech Recognition
Abstract:
Assuming that an specific audio signal, such as recordings of animal sounds, may be modelled as an addition of nonlinear chirps, we use the quadratic energy distribution corresponding to the Chirplet Transform of the signal to produce estimates of the corresponding instantaneous frequencies, chirp-rates and amplitudes at each instant of the recording and design an algorithm for tracking and separating the chirp components of the signal. We demonstrate the accuracy of our algorithm applying it to some synthetic and field recorded signals.