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Time delay estimation is essential in Acoustic Source Localization (ASL)systems. One of the most used techniques for this purpose is the GeneralizedCross Correlation (GCC) between a pair of signals and its use in SteeredResponse Power (SRP) techniques, which estimate the acoustic power at aspecific location. Nowadays, Deep Learning strategies may outperform thesemethods. However, they are generally dependent on the geometric and sensorconfiguration conditions that are available during the training phases, thushaving limited generalization capabilities when facing new environments if nore-training nor adaptation is applied. In this work, we propose a method basedon an encoder-decoder CNN architecture capable of outperforming the well knownSRP-PHAT algorithm, and also other Deep Learning strategies when working inmismatched training-testing conditions without requiring a model re-training.Our proposal aims to estimate a smoothed version of the correlation signals,that is then used to generate a refined acoustic power map, which leads tobetter performance on the ASL task. Our experimental evaluation uses threepublicly available realistic datasets and provides a comparison with theSRP-PHAT algorithm and other recent proposals based on Deep Learning.

Towards Domain Independence in CNN-based Acoustic Localization using Deep Cross Correlations