IEEE Transactions on Pattern Analysis and Machine Intelligence
The fusion of distributed microphone arrays for sound localization
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
Distributed EM algorithms for density estimation and clustering in sensor networks
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Signal enhancement using beamforming and nonstationarity withapplications to speech
IEEE Transactions on Signal Processing
Reduced-Bandwidth and Distributed MWF-Based Noise Reduction Algorithms for Binaural Hearing Aids
IEEE Transactions on Audio, Speech, and Language Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Image Processing
Distributed EM Algorithm for Gaussian Mixtures in Sensor Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Signal Processing
An Integrated Solution for Online Multichannel Noise Tracking and Reduction
IEEE Transactions on Audio, Speech, and Language Processing
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In distributed microphone arrays (DMAs) the source location information can be defined at the intra and inter-node levels. Indeed, while the first type of information results from the diversity of acoustic channels recorded by microphones embedded in the same node, the second is attributed to the differences between the acoustic channels observed by spatially distributed nodes. Both cues are very useful in DMA processing, and the aim of this paper is to utilize both of them to cluster and separate multiple competing speech signals. To capture the intra-node information, we employ the normalized recording vector, while at the inter-node level, we consider different features including the energy level differences with and without the phase differences between nodes. We model the intra-node information using the Watson mixture model (WMM), and propose using the Gamma mixture model (GaMM), Dirichlet mixture model (DMM), and WMM to model different inter-node location features. Furthermore, we propose several integrations of the intra-node and inter-node feature contributions to cluster speech recordings using the expectation maximization algorithm. Finally, simulation results are provided to demonstrate the performance of all ensuing methods.