Music Arena: Live Evaluation for Text-to-Music
Authors: name: Yonghyun Kim, name: Wayne Chi, name: Anastasios N. Angelopoulos, name: Wei-Lin Chiang, name: Koichi Saito, name: Shinji Watanabe, name: Yuki Mitsufuji, name: Chris Donahue

We present Music Arena, an open platform for scalable human preference
evaluation of text-to-music (TTM) models. Soliciting human preferences via
listening studies is the gold standard for evaluation in TTM, but these studies
are expensive to conduct and difficult to compare, as study protocols may
differ across systems. Moreover, human preferences might help researchers align
their TTM systems or improve automatic evaluation metrics, but an open and
renewable source of preferences does not currently exist. We aim to fill these
gaps by offering *live* evaluation for TTM. In Music Arena, real-world users
input text prompts of their choosing and compare outputs from two TTM systems,
and their preferences are used to compile a leaderboard. While Music Arena
follows recent evaluation trends in other AI domains, we also design it with
key features tailored to music: an LLM-based routing system to navigate the
heterogeneous type signatures of TTM systems, and the collection of *detailed*
preferences including listening data and natural language feedback. We also
propose a rolling data release policy with user privacy guarantees, providing a
renewable source of preference data and increasing platform transparency.
Through its standardized evaluation protocol, transparent data access policies,
and music-specific features, Music Arena not only addresses key challenges in
the TTM ecosystem but also demonstrates how live evaluation can be thoughtfully
adapted to unique characteristics of specific AI domains.
  Music Arena is available at: https://music-arena.org