  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.
