  The expressive nature of the voice provides a powerful medium for
communicating sonic ideas, motivating recent research on methods for query by
vocalisation. Meanwhile, deep learning methods have demonstrated
state-of-the-art results for matching vocal imitations to imitated sounds, yet
little is known about how well learned features represent the perceptual
similarity between vocalisations and queried sounds. In this paper, we address
this question using similarity ratings between vocal imitations and imitated
drum sounds. We use a linear mixed effect regression model to show how features
learned by convolutional auto-encoders (CAEs) perform as predictors for
perceptual similarity between sounds. Our experiments show that CAEs outperform
three baseline feature sets (spectrogram-based representations, MFCCs, and
temporal features) at predicting the subjective similarity ratings. We also
investigate how the size and shape of the encoded layer effects the predictive
power of the learned features. The results show that preservation of temporal
information is more important than spectral resolution for this application.
