Sparse Decomposition, Clustering and Noise for Fire Texture Sound Re-Synthesis
In this paper we introduce a framework that represents environmental texture sounds as a linear superposition of independent foreground and background layers that roughly correspond to entities in the physical production of the sound. Sound samples are decomposed into a sparse representation with the matching pursuit algorithm and a dictionary of Daubechies wavelet atoms. An agglomerative clustering procedure groups atoms into short transient molecules. A foreground layer is generated by sampling these sound molecules from a distribution, whose parameters are estimated from the input sample. The residual signal is modelled by an LPC-based source-filter model, synthesizing the background sound layer. The capability of the system is demonstrated with a set of fire sounds.