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In this paper, we study the performance of few-shot learning, specificallymeta learning empowered few-shot relation networks, over classic supervisedlearning in the problem of sound source distance estimation(SSDE). In previousresearch on deep supervised SSDE, obtaining low accuracies due to the mismatchbetween the training data(sound from known environments) and the testdata(sound from unknown environments) has almost always been the case. Byperforming comparative experiments on a sufficient amount of data, we show thatthe few-shot relation network outperform a classic CNN which is a superviseddeep learning approach, and hence it is possible to calibrate amicrophone-equipped system, with a few labeled examples of audio recorded in aparticular unknown environment to adjust and generalize our classifier to thepossible input data and gain higher accuracies.

Few-Shot Sound Source Distance Estimation Using Relation Networks