The first MuseCP evaluation framework is introduced that covers four categories of music facets with fine-grained and well-tailored metrics to capture nuanced changes in music attributes and hopes it can offer practical guidance for developing more effective and reliable music editing strategies with strong MuseCP capability.
Abstract
Music editing plays a vital role in modern music production, with applications in film, broadcasting, and game development. Recent advances in music editing systems have enabled diverse editing tasks such as timbre transfer, instrument substitution, and genre transformation. However, many existing works overlook evaluating their ability to preserve musical facets that should remain unchanged during editing, which we define as Music Context Preservation (MuseCP). While some studies do consider MuseCP, their evaluation protocols and metrics are not comprehensive. To address this, we introduce the first MuseCP evaluation framework, MuseCPEval, that covers four categories of music facets with fine-grained and well-tailored metrics to capture nuanced changes in music attributes. Objective validation and a human study demonstrate the effectiveness of these metrics. Moreover, the case studies on diverse music editing systems illustrate the practical utility of these metrics as a testbed and diagnostic tool, providing insights into the strengths and limitations of existing systems. We hope our metrics and findings can offer practical guidance for developing more effective and reliable music editing strategies with strong MuseCP capability
MusGU+ is introduced, a musician-centered evaluation framework organized around three dimensions: Adaptability, Usability, and Controllability that supports informed selection and practical adoption of generative systems by musicians.
Laura Ibáñez-Martínez, Roser Batlle-Roca, Xavier Serra et al.· 1 citation
Automatic music transcription systems produce sheet music that can be read and played back. We argue that these two targets call for complementary evaluations of notation similarity to a reference score and playback similarity to the original performance, respectively. Our study considers notation similarity metrics fr...
Ping Wang, Guang Yang, N. Tamer et al.· 0 citations
Music creation is fundamentally a process of revision. Yet symbolic music generation remains dominated by paradigms that produce complete sequences from scratch, with limited support for selective modification. Edit-based methods have proven effective for text transformation tasks, but remain largely unexplored for sym...
Haoyu Gu, Lekai Qian, Haowu Zhou et al.· 0 citations
Music visualization offers a powerful way to enhance listeners'understanding and experience of music by translating auditory signals into visual forms. However, most existing approaches either rely heavily on lyrics or generate flat, non-immersive videos similar to conventional music videos, which limits their ability...
K. Tsai, Yongyong Fu, Hung I Yang et al.· 0 citations
MIDI-to-Music system renders the melody and rhythm of a target MIDI sequence into musical segment while cloning instrument timbre from a prompt recording. Existing systems typically adopt one of two distinct paradigms: conditional generation with prompt audio alone, which remains applicable when aligned prompt MIDI is...
Chong Jing, Junan Zhang, Jing Yang et al.· 0 citations
MusiChat is presented, a conversational vibe composing system that enables collaborative human-AI music creation through natural-language interaction and iterative refinement, and supports coherent multi-turn music authoring and interactive human-AI co-creation through a conversational interface.
Callie C. Liao, Duoduo Liao, Ellie L. Zhang· arXiv.org· 1 citation
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