Tempo estimation as fully self-supervised binary classification
Florian Henkel Jaehun Kim Matthew C. McCallum Samuel E. Sandberg Matthew E. P. Davies
This paper addresses the problem of global tempo estimation in musical audio.
Given that annotating tempo is time-consuming and requires certain musical
expertise, few publicly available data sources exist to train machine learning
models for this task. Towards alleviating this issue, we propose a fully
self-supervised approach that does not rely on any human labeled data. Our
method builds on the fact that generic (music) audio embeddings already encode
a variety of properties, including information about tempo, making them easily
adaptable for downstream tasks. While recent work in self-supervised tempo
estimation aimed to learn a tempo specific representation that was subsequently
used to train a supervised classifier, we reformulate the task into the binary
classification problem of predicting whether a target track has the same or a
different tempo compared to a reference. While the former still requires
labeled training data for the final classification model, our approach uses
arbitrary unlabeled music data in combination with time-stretching for model
training as well as a small set of synthetically created reference samples for
predicting the final tempo. Evaluation of our approach in comparison with the
state-of-the-art reveals highly competitive performance when the constraint of
finding the precise tempo octave is relaxed.