K-shot learning of acoustic context


In order to personalize the behavior of hearing aid devices in different acoustic scenes, we need personalized acoustic scene classifiers. Since we cannot afford to burden an individual hearing aid user with the task to collect a large acoustic database, we will want to train an acoustic scene classifier on one in-situ recorded waveform (of a few seconds duration) per class. In this paper we develop a method that achieves high levels of classification accuracy from a single recording of an acoustic scene.

NIPS Workshop on Machine Learning for Audio Signal Processing