SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation
Abstract
Cover songs constitute a vital aspect of musical culture, preserving the core melody of an original composition while reinterpreting it to infuse novel emotional depth and thematic emphasis. Although prior research has explored the reinterpretation of instrumental music through melody-conditioned text-to-music models, the task of cover song generation remains largely unaddressed. In this work, we reformulate our cover song generation as a conditional generation, which simultaneously generates new vocals and accompaniment conditioned on the original vocal melody and text prompts. To this end, we present SongEcho, which leverages Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), a framework that incorporates controllable generation by improving both conditioning injection mechanism and conditional representation. To enhance the conditioning injection mechanism, we extend Feature-wise Linear Modulation (FiLM) to an Element-wise Linear Modulation (EiLM), to facilitate precise temporal alignment in melody control. For conditional representations, we propose Instance-Adaptive Condition Refinement (IACR), which refines conditioning features by interacting with the hidden states of the generative model, yielding instance-adaptive conditioning. Additionally, to address the scarcity of large-scale, open-source full-song datasets, we construct Suno70k, a high-quality AI song dataset enriched with comprehensive annotations. Experimental results across multiple datasets demonstrate that our approach generates superior cover songs compared to existing methods, while requiring fewer than 30% of the trainable parameters. The code, dataset, and demos are available at https://github.com/lsfhuihuiff/SongEcho_ICLR2026.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to cover song generation through the introduction of IA-EiLM and IACR, significantly advancing the field of audio machine learning. The methodology and experimental results indicate a strong potential for practical applications in music generation, although further work is needed to enhance reproducibility and address limitations.
Methodology
The methodology presented in the paper is innovative, particularly with the introduction of Instance-Adaptive Element-wise Linear Modulation (IA-EiLM) and Instance-Adaptive Condition Refinement (IACR). The extension of Feature-wise Linear Modulation (FiLM) to EiLM is a notable advancement that addresses the challenge of temporal alignment in melody control. The dual focus on generating both vocals and accompaniment conditioned on the original melody and text prompts is a significant step forward in cover song generation. However, the paper could benefit from a more detailed explanation of the underlying mechanics of IA-EiLM and IACR, particularly how they interact with the generative model's hidden states.
Results & evaluation
The experimental results are compelling, demonstrating that SongEcho outperforms existing methods while utilizing fewer parameters, which suggests a more efficient model. The construction of the Suno70k dataset is a valuable contribution, addressing a critical gap in the availability of high-quality, annotated datasets for song generation tasks. However, the paper should provide more comprehensive comparisons with a wider range of baseline methods to strengthen the claims of superiority.
Reproducibility
The authors have made the code and dataset publicly available, which is a positive aspect for reproducibility. However, the paper lacks detailed implementation instructions and hyperparameter settings, which could hinder other researchers from replicating the results accurately.
Limitations
One limitation of the study is the potential overfitting due to the small size of the dataset relative to the complexity of the task. Additionally, the subjective nature of music generation means that quantitative metrics may not fully capture the quality of the generated songs. The paper could also explore the limitations of the IA-EiLM and IACR methods in different musical contexts or genres.
Broader impact
The proposed framework has significant implications for the music industry, particularly in automated music composition and cover song generation. By enabling more nuanced and emotionally resonant reinterpretations of existing songs, this research could enhance creative processes in music production. Furthermore, the availability of the Suno70k dataset could spur further research in music generation and related fields. The paper presents a novel approach to cover song generation through the introduction of IA-EiLM and IACR, significantly advancing the field of audio machine learning. The methodology and experimental results indicate a strong potential for practical applications in music generation, although further work is needed to enhance reproducibility and address limitations.
Author of this paper? Send a correction.