Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
AV16.3: an audio-visual corpus for speaker localization and tracking
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Blind Source Separation Exploiting Higher-Order Frequency Dependencies
IEEE Transactions on Audio, Speech, and Language Processing
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The machine cocktail party problem has been researched for several decades. Although many blind source separation schemes have been proposed to address this problem, few of them are tested by using a real room audio video recording. In this paper, we propose an audio video based independent vector analysis (AVIVA) method, and test it with other independent vector analysis methods by using a real room recording dataset, i.e. the AV16.3 corpus. Moreover, we also use a new method based on pitch difference detection for objective evaluation of the separation performance of the algorithms when applied on the real dataset which confirms advantages of using the visual modality with IVA.