Clustering and suppression of transient noise in speech signals using diffusion maps

Recently we have presented a novel approach for transient noise reduction that relies on non-local (NL) filtering. In this paper, we modify and extend our approach to support clustering and suppression of a few transient noise types simultaneously, by introducing two novel concepts. We observe that voiced speech spectral components are slowly varying compared to transient noise. Thus, by applying an algorithm for noise power spectral density (PSD) estimation, configured to track faster variations than pseudo-stationary noise, the PSD of speech components may be estimated. In addition, we utilize diffusion maps to embed the measurements into a new domain. We obtain a new representation which enables clustering of different transient noise types. The new representation is incorporated into a NL filter as a better affinity metric for averaging over transient instances. Experimental results show that the proposed algorithm enables clustering and suppression of multiple transient interferences.