Learning-based acoustic source-microphone distance estimation using the coherent-to-diffuse power ratio

We propose a method for estimating the distance between a sound source and a pair of recording microphones. The developed algorithm operates in the short-time Fourier transform domain and is based on estimates of the coherent-to-diffuse power ratio, which provides a measure for the amount of reverberation in each time-frequency bin. For a direct use of these estimates, precise knowledge on the room characteristics is necessary, which is in practice usually not available and hard to obtain. Therefore, we use a learning-based method, which adapts to the characteristics of the room in a training phase and estimates the source-microphone distance in a testing phase. The experiments comprise various setups with simulated and real data. It is shown that the proposed method generalizes well for different microphone positions and works robustly for different source signals, directions of arrival, reverberation times, and signal observation intervals. This leads to a high estimation accuracy at a low computational complexity with a small amount of training data.