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Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks

2020

The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized cross-correlations (GCCs) have been widely used for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a novel technique for TDE based on a sub-band analysis of the cross-power spectrum phase, providing a structured two-dimensional representation of the time delay information contained across different frequency bands. Inspired by deep-learning-based image denoising solutions, we propose in this paper the use of convolutio…

FOS: Computer and information sciencesSound (cs.SD)Computer sciencePhase (waves)Distributed microphones02 engineering and technologyConvolutional neural networkComputer Science - Sound030507 speech-language pathology & audiology03 medical and health sciencesAudio and Speech Processing (eess.AS)FOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringGCCRepresentation (mathematics)Signal processingbusiness.industryI.5.4Deep learningConvolutional Neural Networks020206 networking & telecommunicationsTime delay estimationMultilaterationI.2.094A12 68T10LocalizationArtificial intelligence0305 other medical sciencebusinessAlgorithmElectrical Engineering and Systems Science - Audio and Speech ProcessingI.2.0; I.5.4ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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