4 papers · 1 filter
Controllable Embedding Transformation for Mood-Guided Music Retrieval
Julia Wilkins, Jaehun Kim, Matthew E. P. Davies +2
Music representations are the backbone of modern recommendation systems, powering playlist generation, similarity search, and personalized discovery. Yet most embeddings offer litt…
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search
Matthew C. McCallum, Florian Henkel, Jaehun Kim +2
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…
Tempo estimation as fully self-supervised binary classification
Florian Henkel, Jaehun Kim, Matthew C. McCallum +2
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 ava…
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations
Matthew C. McCallum, Matthew E. P. Davies, Florian Henkel +2
Audio embeddings are crucial tools in understanding large catalogs of music. Typically embeddings are evaluated on the basis of the performance they provide in a wide range of down…