Extending GCC-PHAT using Shift Equivariant Neural Networks
arXiv:2208.04654 · doi:10.21437/Interspeech.2022-524
Abstract
Speaker localization using microphone arrays depends on accurate time delay estimation techniques. For decades, methods based on the generalized cross correlation with phase transform (GCC-PHAT) have been widely adopted for this purpose. Recently, the GCC-PHAT has also been used to provide input features to neural networks in order to remove the effects of noise and reverberation, but at the cost of losing theoretical guarantees in noise-free conditions. We propose a novel approach to extending the GCC-PHAT, where the received signals are filtered using a shift equivariant neural network that preserves the timing information contained in the signals. By extensive experiments we show that our model consistently reduces the error of the GCC-PHAT in adverse environments, with guarantees of exact time delay recovery in ideal conditions.
arXiv commentProceedings of INTERSPEECH
References in corpus (8)
- Adam: A Method for Stochastic Optimization
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Pyroomacoustics: A Python package for audio room simulations and array processing algorithms
- A Survey of Sound Source Localization with Deep Learning Methods
- Broadband DOA estimation using Convolutional neural networks trained with noise signals
- Deep Neural Networks for Multiple Speaker Detection and Localization
- Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
- TDOA Matrices: Algebraic Properties and their Application to Robust Denoising with Missing Data