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arXiv · 2607.26698

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

Wei-Jaw Lee·Hsuan-Yu Yeh·Ting-Yi Hu·Chih-Pin Tan·Fang-Duo Tsai·Yi-Hsuan Yang
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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 https://lonian6.github.io/MPEcho.github.io/.

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Wei-Jaw Lee, Hsuan-Yu Yeh, Ting-Yi Hu, Chih-Pin Tan, Fang-Duo Tsai, Yi-Hsuan Yang. 2026-07-29. MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation. https://arxiv.org/abs/2607.26698

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