Christensen, JEN and Godsill, SJ (2011) Bayesian classification of acoustical waveforms under environmental variability. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. pp. 281-284.Full text not available from this repository.
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. © 2011 IEEE.
|Divisions:||Div F > Signal Processing and Communications|
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|Date Deposited:||09 Dec 2016 18:26|
|Last Modified:||22 Jan 2017 23:51|