Characterisation of acoustic scenes using a temporally-constrained shift-invariant model
Emmanouil Benetos, Mathieu Lagrange, Simon Dixon · City Research Online (City University London) · 2012
In this paper, we propose a method for modeling and classifying acoustic scenes using temporally-constrained shift-invariant probabilistic latent component analysis (SIPLCA).SIPLCA can be used for extracting time-frequency patches from spectrograms in an unsupervised manner.Component-wise hidden Markov models are incorporated to the SIPLCA formulation for enforcing temporal constraints on the activation of each acoustic component.The time-frequency patches are converted to cepstral coefficients in order to provide a compact representation of acoustic events within a scene.Experiments are made using a corpus of train station recordings, classified into 6 scene classes.Results show that the proposed model is able to model salient events within a scene and outperforms the non-negative matrix factorization algorithm for the same task.In addition, it is demonstrated that the use of temporal constraints can lead to improved performance.