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Matthew C. McCallum

7 accepted papers

2026

Controllable Embedding Transformation for Mood-Guided Music Retrieval

ICASSP 2026poster

Music representations are the backbone of modern recommendation systems, powering playlist generation, similarity search, and personalized discovery. Yet most embeddings offer little control for adjusting a single musical attribute, e.g., changing only the mood of a track while preserving its genre…

Cited by 1SourcePDFScholar
2024

On The Effect Of Data-Augmentation On Local Embedding Properties In The Contrastive Learning Of Music Audio Representations

ICASSP 2024accepted

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 downstream tasks, however few studies have investigated the local properties of the embedding spaces themselves which are im…

Cited by 10SourceScholar
2024

Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search

ICASSP 2024accepted

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. t…

Cited by 0SourceScholar
2024

Tempo Estimation as Fully Self-Supervised Binary Classification

ICASSP 2024accepted

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 f…

Cited by 2SourceScholar
2022

A Novel 1D State Space for Efficient Music Rhythmic Analysis

ICASSP 2022accepted

Inferring music time structures has a broad range of applications in music production, processing and analysis. Scholars have proposed various methods to analyze different aspects of time structures, such as beat, downbeat, tempo and meter. Many state-of-the-art (SOFA) methods, however, are computat…

Cited by 0SourceScholar