Robust regression and outlier detection
Robust regression and outlier detection
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The image processing handbook (3rd ed.)
The image processing handbook (3rd ed.)
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A probabilistic approach for blind source separation of underdetermined convolutive mixtures
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
Underdetermined blind source separation in echoic environments using DESPRIT
EURASIP Journal on Applied Signal Processing
A cross-validation framework for solving image restoration problems
Journal of Visual Communication and Image Representation
Blind separation of sparse sources using jeffrey’s inverse prior and the EM algorithm
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Fuzzy order statistics and their application to fuzzy clustering
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
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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.