Andrew Sarroff

Groove Kernels as Rhythmic-Acoustic Motif Descriptors

Proceedings of the International Society for Music Information Retrieval
The “groove” of a song correlates with enjoyment and bodily movement. Recent work has shown that humans often agree whether a song does or does not have groove and how much groove a song has. It is therefore useful to develop algorithms that characterize the quality of groove across songs. We evaluate three unsupervised tempo-invariant models for measuring pairwise musical groove similarity: A temporal model, a timbre-temporal model, and a pitch-timbre-temporal model.

Musical Audio Synthesis Using Autoencoding Neural Networks

Proceedings of the International Computer Music Conference
With an optimal network topology and tuning of hyperparameters, artificial neural networks (ANNs) may be trained to learn a mapping from low level audio features to one or more higher-level representations. Such artificial neural networks are commonly used in classification and re-gression settings to perform arbitrary tasks. In this work we suggest re-purposing auto-encoding neural networks as musical audio synthesizers.