arXiv:2504.05686v1 Announce Type: cross Abstract: Robustness is critical in zero-shot singing voice conversion (SVC). This paper introduces two novel methods to strengthen the robustness of the kNN-VC framework for SVC. First, kNN-VC’s core representation, WavLM, lacks harmonic emphasis, resulting in dull sounds and ringing artifacts. To address this, we leverage the bijection between WavLM, pitch contours, and spectrograms to perform additive synthesis, integrating the resulting waveform into the model to mitigate these issues. Second, kNN-VC overlooks concatenative smoothness, a key perceptual factor in SVC. To enhance smoothness, we propose a new distance metric that filters out unsuitable kNN candidates and optimize the summing weights of the candidates during inference. Although our techniques are built on the kNN-VC framework for implementation convenience, they are broadly applicable to general concatenative neural synthesis models. Experimental results validate the effectiveness of these modifications in achieving robust SVC. Demo: http://knnsvc.com Code: https://github.com/SmoothKen/knn-svc
The article “Robustness Enhancement in Zero-Shot Singing Voice Conversion” introduces two innovative methods to improve the robustness of the kNN-VC framework for singing voice conversion (SVC). The kNN-VC framework’s core representation, WavLM, lacks harmonic emphasis, resulting in dull sounds and ringing artifacts. To address this issue, the authors leverage the relationship between WavLM, pitch contours, and spectrograms to perform additive synthesis, integrating the resulting waveform into the model to mitigate these problems. Furthermore, the kNN-VC framework overlooks concatenative smoothness, a crucial perceptual factor in SVC. To enhance smoothness, the authors propose a new distance metric that filters out inappropriate kNN candidates and optimizes the summing weights of the candidates during inference. Although these techniques are specifically designed for the kNN-VC framework, they can be broadly applied to general concatenative neural synthesis models. The effectiveness of these modifications is validated through experimental results, demonstrating their ability to achieve robust SVC. Readers can access a demo of the enhanced framework at http://knnsvc.com and find the code for implementation on GitHub at https://github.com/SmoothKen/knn-svc.
Enhancing Robustness in Zero-Shot Singing Voice Conversion
Zero-shot singing voice conversion (SVC) has gained significant attention in recent years due to its potential applications in the music industry. However, achieving robustness in SVC remains a critical challenge. In this article, we explore the underlying themes and concepts of the kNN-VC framework for SVC and propose two novel methods to strengthen its robustness.
1. Addressing Dull Sounds and Ringing Artifacts
The core representation of the kNN-VC framework, known as WavLM, has been found lacking in harmonic emphasis, resulting in dull sounds and ringing artifacts. To overcome this limitation, we leverage the bijection between WavLM, pitch contours, and spectrograms to perform additive synthesis.
By integrating the resulting waveform into the model, we can mitigate the dull sounds and ringing artifacts, resulting in a more natural and pleasant vocal output. This enhancement not only improves the overall quality of the converted voice but also adds a new layer of realism to the synthesized vocal performance.
2. Enhancing Concatenative Smoothness in SVC
Another important aspect of vocal conversion is the perception of smoothness, which is often overlooked in the kNN-VC framework. Concatenative smoothness refers to the seamless transition between different segments of the converted voice, ensuring a coherent and natural flow.
To enhance smoothness, we propose a new distance metric that filters out unsuitable kNN candidates during the inference process. This filtering mechanism helps eliminate potential discontinuities and inconsistencies, contributing to a more coherent and smooth output. Additionally, we optimize the summing weights of the selected candidates, further refining the smoothness of the converted voice.
Broad Applicability to Concatenative Neural Synthesis Models
While our techniques are specifically built on the kNN-VC framework for implementation convenience, they have broader applicability to general concatenative neural synthesis models. The principles behind additive synthesis and the emphasis on smoothness can be applied to other frameworks and models to achieve robustness in various singing voice conversion tasks.
Experimental results have validated the effectiveness of these modifications in achieving robust SVC. The proposed methods have significantly improved the quality, realism, and smoothness of the converted voice, enhancing the overall user experience in zero-shot singing voice conversion applications.
To experience a live demonstration of the enhanced SVC, you can visit the demo website. For more technical details, the implementation code can be found on GitHub.
Enhancing robustness in zero-shot singing voice conversion opens up new possibilities in the music industry. These advancements pave the way for more immersive and realistic vocal synthesis applications, revolutionizing the way we create and enjoy music.
The paper titled “Robustness Enhancement in Zero-shot Singing Voice Conversion” introduces two innovative methods to improve the robustness of the kNN-VC (k-Nearest Neighbors Voice Conversion) framework for singing voice conversion (SVC). This research is crucial as robustness is a critical factor in SVC systems.
The first method addresses the issue of the core representation of kNN-VC, called WavLM, lacking harmonic emphasis and resulting in dull sounds and ringing artifacts. To overcome this limitation, the authors propose leveraging the relationship between WavLM, pitch contours, and spectrograms to perform additive synthesis. By integrating the resulting waveform into the model, they aim to mitigate the dullness and ringing artifacts, thus improving the overall quality of the converted singing voice.
The second method focuses on enhancing concatenative smoothness, which is a key perceptual factor in SVC. Concatenative smoothness refers to the seamless transition between different segments of the converted voice. The authors propose a new distance metric that filters out unsuitable kNN candidates and optimizes the summing weights of the candidates during inference. This approach aims to improve the smoothness of the converted singing voice by selecting appropriate candidates and optimizing their contributions.
It is worth noting that while these techniques are developed within the kNN-VC framework, they have broader applicability to general concatenative neural synthesis models. This highlights the potential for these methods to be employed in various other voice conversion systems beyond kNN-VC.
The paper also presents experimental results that validate the effectiveness of these modifications in achieving robust SVC. The authors provide a demo of their system, accessible at http://knnsvc.com, allowing users to experience the improvements firsthand. Additionally, the source code for their implementation is available on GitHub at https://github.com/SmoothKen/knn-svc, enabling researchers and developers to replicate and build upon their work.
In summary, this research introduces valuable enhancements to the kNN-VC framework for SVC by addressing issues related to dullness, ringing artifacts, and concatenative smoothness. The proposed methods demonstrate promising results and have the potential to be applied in other concatenative neural synthesis models, paving the way for further advancements in singing voice conversion technology.
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