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Localizing a moving sound source in the real world involves determining itsdirection-of-arrival (DOA) and distance relative to a microphone. Advancementsin DOA estimation have been facilitated by data-driven methods optimized withlarge open-source datasets with microphone array recordings in diverseenvironments. In contrast, estimating a sound source's distance remainsunderstudied. Existing approaches assume recordings by non-coincidentmicrophones to use methods that are susceptible to differences in roomreverberation. We present a CRNN able to estimate the distance of moving soundsources across multiple datasets featuring diverse rooms, outperforming arecently-published approach. We also characterize our model's performance as afunction of sound source distance and different training losses. This analysisreveals optimal training using a loss that weighs model errors as an inversefunction of the sound source true distance. Our study is the first todemonstrate that sound source distance estimation can be performed acrossdiverse acoustic conditions using deep learning.

Sound Source Distance Estimation in Diverse and Dynamic Acoustic Conditions