Enforcing equal CNN hidden activations for parallel recordings from different microphones improves target-device accuracy in acoustic scene classification and beats MMD-based domain adaptation in these experiments.
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification
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abstract
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in the context of machine listening (Task 1b of the DCASE 2019 Challenge). We propose a novel approach to learn domain-invariant classifiers in an end-to-end fashion by enforcing equal hidden layer representations for domain-parallel samples, i.e. time-aligned recordings from different recording devices. No classification labels are needed for our domain adaptation (DA) method, which makes the data collection process cheaper.
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Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification
Enforcing equal CNN hidden activations for parallel recordings from different microphones improves target-device accuracy in acoustic scene classification and beats MMD-based domain adaptation in these experiments.