A generative model for natural sounds based on latent force modelling

Abstract

Generative models based on subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitude modulation to be a crucial component of auditory perception. Probabilistic latent variable analysis can be particularly insightful, but existing approaches don’t incorporate prior knowledge about the physical behaviour of amplitude envelopes, such as exponential decay or feedback. We use latent force modelling, a probabilistic learning paradigm that encodes physical knowledge into Gaussian process regression, to model correlation across spectral subband envelopes. We augment the standard latent force model approach by explicitly modelling dependencies across multiple time steps. Incorporating this prior knowledge strengthens the interpretation of the latent functions as the source that generated the signal. We examine this interpretation via an experiment showing that sounds generated by sampling from our probabilistic model are perceived to be more realistic than those generated by comparative models based on nonnegative matrix factorisation, even in cases where our model is outperformed from a reconstruction error perspective.