Authors

Venue

Date

Share

Discogs-VINet-MIREX

Xavier Serra

Yuki Mitsufuji

R.O. Araz

J. Serrà

D. Bogdanov

MIREX 2024

2025

Abstract

This technical report presents our submission to the cover song identification task for the 2024 edition of the Music Information Retrieval Evaluation eXchange (MIREX). For this submission, we enhanced our Discogs-VINet model by changing the definition of an epoch, incorporating automatic mixed precision (AMP) during both training and inference, and sampling four versions per clique during triplet mining (which became possible with AMP). Due to this enhanced model’s performance on the Discogs-VI test set, we trained a new model from scratch using the entire Discogs-VI dataset, rather than just the training partition used in Discogs-VINet (a 45% increase in the number of versions). This enhanced and retrained model is named Discogs-VINet-MIREX.

Related Publications

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

Interspeech, 2025
Yigitcan Özer, Woosung Choi, Joan Serrà, Mayank Kumar Singh*, Wei-Hsiang Liao, Yuki Mitsufuji

We introduce the Robust Audio Watermarking Benchmark (RAW-Bench), a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic comparisons. To simulate real-world usage, we introduce a comprehensive audio attack pipeline with var…

Training Consistency Models with Variational Noise Coupling

ICML, 2025
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji

Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and impr…

Supervised Contrastive Learning from Weakly-labeled Audio Segments for Musical Version Matching

ICML, 2025
Joan Serrà, R. Oguz Araz, Dmitry Bogdanov, Yuki Mitsufuji

Detecting musical versions (different renditions of the same piece) is a challenging task with important applications. Because of the ground truth nature, existing approaches match musical versions at the track level (e.g., whole song). However, most applications require to …

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.