A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separation

Abstract

Time-frequency masking has evolved as a powerful tool for tackling blind source separation problems. In previous work, mask estimation was performed with the help of well-known standard cluster algorithms. Spatial observation vectors, extracted from a set of microphones, were grouped into separate clusters, each representing a particular source. However, most off-the-shelf clustering methods are not very robust to outliers or noise in the data. This lack of robustness often leads to incorrect localization and partitioning results, particularly for reverberant speech mixtures. To address this issue, we investigate the use of observation weights and context information as means to improve the clustering performance under reverberant conditions. While the observation weights improve the localization accuracy by ignoring noisy observations, context information smoothes the cluster membership levels by exploiting the highly structured nature of speech signals in the time-frequency domain. In a number of experiments, we demonstrate the superiority of the proposed method over conventional fuzzy clustering, both in terms of localization accuracy as well as speech separation performance.

Advancements in the Time-Frequency Approach to Multichannel Blind Source Separation

“…Whilst this binary clustering performed satisfactorily in both simulated and realistic reverberant environments, the authors of (Jafari et al, 2011;Kühne et al, 2010) demonstrate that the application of a soft masking scheme improves the separation performance substantially. …”

“…The authors of (Araki et al, 2006a) suggest that fuzzy TF masking approaches bear the potential to reduce the musical noise at the output significantly. In (Kühne et al, 2010) the use of the fuzzy c-means clustering for mask estimation was investigated in the TF masking framework of BSS; on the contrary to MENUET, this approaches integrated a fuzzy partitioning in the clustering in order to model the inherent ambiguity surrounding the membership of a TF cell to a cluster. Examples of contributing factors to such ambiguous conditions include the effects of reverberation and additive channel noise at the sensors in the array.…”

“…Motivated by these shortcomings, this chapter presents an extension of the MENUET algorithm via a novel amalgamation with the FCM as in (Kühne et al, 2010) (see Fig. 1).…”

“…1). The applicability of MENUET to underdetermined and arbitrary sensor constellations renders it superior in many scenarios over the investigation in (Kühne et al, 2010); however, its performance is hindered by its non-robust approach to mask estimation. Firstly, this study proposes that the combination of fuzzy clustering with the MENUET algorithm, which will henceforth be denoted as MENUET-FCM, will improve the separation performance in reverberant conditions.…”

“…Whilst this assumption holds true for anechoic mixtures, as the reverberation in the acoustic scene increases it becomes increasingly unreliable due to the effects of multipath propagation and multiple reflections (Kühne et al, 2010;Yilmaz & Rickard, 2004).…”