3 citations · 3 across the 4 of their papers we have counts for
4 papers
Semantic IDs for Music Recommendation
M. Jeffrey Mei, Florian Henkel, Samuel E. Sandberg +2
Training recommender systems for next-item recommendation often requires unique embeddings to be learned for each item, which may take up most of the trainable parameters for a mod…
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…