Title :
Bayesian classification of acoustical waveforms under environmental variability
Author :
Christensen, Jens E N ; Godsill, Simon J.
Author_Institution :
Dept. of Eng., Cambridge Univ., Cambridge, UK
Abstract :
We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability.
Keywords :
Bayes methods; acoustic signal processing; principal component analysis; probability; signal classification; APR; Bayesian classification; PCA; acoustic pulse recognition; acoustical waveforms; environmental variability; multivariate probabilistic approach; principle component analysis; pulse variability; template matching methods; Acoustics; Principal component analysis; Signal processing algorithms; Testing; Training; Training data; Vectors; Acoustic pulse recognition; Bayesian classification; PCA; tangible interface;
Conference_Titel :
Applications of Signal Processing to Audio and Acoustics (WASPAA), 2011 IEEE Workshop on
Conference_Location :
New Paltz, NY
Print_ISBN :
978-1-4577-0692-9
Electronic_ISBN :
1931-1168
DOI :
10.1109/ASPAA.2011.6082283