Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges

TL;DR

Over 25 years, the paper surveys MIR's evolution from signal-processing roots to data-driven, AI-enabled approaches. It analyzes achievements across music analysis, processing, and generation, and reviews benchmarking, reproducibility, industry impact, and DEI practices. It discusses future challenges such as translating AI advances to music understanding, data and cultural diversity, perceptually meaningful evaluation, environmental costs, and copyright concerns. The work highlights how open science, shared benchmarks, and industry collaboration shape MIR's ongoing growth and societal impact.

Abstract

In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large part of it focuses on signal processing techniques for music data, fostering a close relationship with the IEEE Audio and Acoustic Signal Processing Technical Commitee. In this paper, we reflect the main research achievements of MIR along the three EDICS related to music analysis, processing and generation. We then review a set of successful practices that fuel the rapid development of MIR research. One practice is the annual research benchmark, the Music Information Retrieval Evaluation eXchange, where participants compete on a set of research tasks. Another practice is the pursuit of reproducible and open research. The active engagement with industry research and products is another key factor for achieving large societal impacts and motivating younger generations of students to join the field. Last but not the least, the commitment to diversity, equity and inclusion ensures MIR to be a vibrant and open community where various ideas, methodologies, and career pathways collide. We finish by providing future challenges MIR will have to face.