Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for
  Tempo Prediction and Search
Matthew C. McCallum Florian Henkel Jaehun Kim Samuel E. Sandberg Matthew E. P. Davies
Audio embeddings enable large scale comparisons of the similarity of audio
files for applications such as search and recommendation. Due to the
subjectivity of audio similarity, it can be desirable to design systems that
answer not only whether audio is similar, but similar in what way (e.g., wrt.
tempo, mood or genre). Previous works have proposed disentangled embedding
spaces where subspaces representing specific, yet possibly correlated,
attributes can be weighted to emphasize those attributes in downstream tasks.
However, no research has been conducted into the independence of these
subspaces, nor their manipulation, in order to retrieve tracks that are similar
but different in a specific way. Here, we explore the manipulation of tempo in
embedding spaces as a case-study towards this goal. We propose tempo
translation functions that allow for efficient manipulation of tempo within a
pre-existing embedding space whilst maintaining other properties such as genre.
As this translation is specific to tempo it enables retrieval of tracks that
are similar but have specifically different tempi. We show that such a function
can be used as an efficient data augmentation strategy for both training of
downstream tempo predictors, and improved nearest neighbor retrieval of
properties largely independent of tempo.