Underdetermined Blind Source Separation with Fuzzy Clustering for Arbitrarily Arranged Sensors

Recently, the concept of time-frequency masking has developed as an important approach to the blind source separation problem, particularly when in the presence of reverberation. However, previous research has been limited by factors such as the sensor arrangement and/or the mask estimation technique implemented. This paper presents a novel integration of two established approaches to BSS in an effort to overcome such limitations. A multidimensional feature vector is extracted from a non-linear sensor arrangement, and the fuzzy c-means algorithm is then applied to cluster the feature vectors into representations of the source speakers. Fuzzy time-frequency masks are estimated and applied to the observations for source recovery. The evaluations on the proposed study demonstrated improved separation quality over all test conditions. This establishes the potential of multidimensional fuzzy c-means clustering for mask estimation in the context of blind source separation.