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Despite significant advancements in deep learning for vision and naturallanguage, unsupervised domain adaptation in audio remains relativelyunexplored. We, in part, attribute this to the lack of an appropriate benchmarkdataset. To address this gap, we present Synthia's melody, a novel audio datageneration framework capable of simulating an infinite variety of 4-secondmelodies with user-specified confounding structures characterised by musicalkeys, timbre, and loudness. Unlike existing datasets collected underobservational settings, Synthia's melody is free of unobserved biases, ensuringthe reproducibility and comparability of experiments. To showcase its utility,we generate two types of distribution shifts-domain shift and sample selectionbias-and evaluate the performance of acoustic deep learning models under theseshifts. Our evaluations reveal that Synthia's melody provides a robust testbedfor examining the susceptibility of these models to varying levels ofdistribution shift.

Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio