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|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||18 May 2016 19:19|
|Last Modified:||02 Jul 2016 00:30|