Deep learning models improve estimation of sound source transfer matrices

Researchers have developed novel deep learning frameworks for estimating the Relative Transfer Matrix (ReTM), a generalization of the relative transfer function for multiple sources and receivers. The proposed methods utilize time and short-time frequency transform domain convolutional networks, as well as a Long Short-Term Memory-based recurrent neural network. Experiments show these deep learning approaches achieve more accurate ReTM estimation than traditional covariance-based methods, with performance comparable to baseline methods in speech enhancement applications. AI

IMPACT Enhances signal processing techniques for audio applications like speech enhancement.

RANK_REASON Academic paper detailing novel deep learning methods for signal processing. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• deep learning
• long short-term memory
• recurrent neural network
• Relative Transfer Function
• Relative Transfer Matrix

• paper
• other

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