MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation

Abstract:Cover song generation (CSG) should preserve the melodic and linguistic content of a reference song while recreating the remaining musical components. The state-of-the-art model SongEcho utilizes $F_0$ sequences and voiced/unvoiced (V/UV) tags for conditioning; however, implicit linguistic information from V/UV tags cannot guarantee lyric accuracy, leading to a high phoneme error rate (PER). Inspired by singing voice synthesis (SVS), we propose MPEcho, which integrates a phoneme encoder and a length regulator (LR) into the SongEcho framework. By providing explicit phoneme-level conditioning and precise temporal boundaries, MPEcho significantly reduces PER. To enable this, we developed Phonsa, a Whisper-based automatic transcription model that provides high-precision phoneme-level annotations for singing voices, overcoming the scarcity of high-quality audio-phoneme pairs. Experimental results validate the effectiveness of Phonsa for alignment and MPEcho for end-to-end CSG. The audio samples, code and weights can be accessed from this https URL.

Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)

arXiv:2607.26698 [cs.SD]

(or arXiv:2607.26698v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2607.26698 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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