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Singing Voice Beautification with Pitch Correction and Expressiveness Enhancement


Core Concepts
CONTUNER, a diffusion-based model, can efficiently beautify amateur singing voices by correcting pitch and enhancing expressiveness without requiring paired professional-amateur data.
Abstract
The paper proposes CONTUNER, a novel singing voice beautification (SVB) model that combines a diffusion-based generative approach with pitch prediction and expressiveness enhancement. Key highlights: CONTUNER does not require paired professional-amateur data, addressing a key limitation of existing SVB methods. It extracts conditions from amateur singing voices and professional MIDI. The pitch predictor maps MIDI and spectral envelope to obtain a corrected pitch curve, while the expressiveness enhancer modifies the amateur expressiveness to be more professional-like. CONTUNER uses a generator-based diffusion model to efficiently generate the beautified Mel-spectrogram, requiring fewer sampling steps compared to gradient-based approaches. Experiments show CONTUNER achieves high-quality and expressive beautification results on both Mandarin and English singing voices, outperforming baseline methods. Ablation studies demonstrate the effectiveness of the expressiveness enhancer and the generator-based diffusion approach in CONTUNER. Overall, CONTUNER presents a promising solution for automatic singing voice beautification that can be widely applicable in entertainment and music production.
Stats
The pitch alignment accuracy (PAA) of CONTUNER's pitch predictor is higher than classic time-warping algorithms like DTW and CTW, as well as the Transformer-based KaraTuner method. CONTUNER achieves MOS-Q (audio quality) scores of 4.21 and 4.06 on Mandarin and English data respectively, which are close to the ground-truth professional recordings. CONTUNER's MOS-E (expressiveness) scores of 4.24 and 4.03 on Mandarin and English data are also very close to the ground-truth professional recordings. The Mel-cepstral distortion (MCD) of CONTUNER is lower than the baseline KaraTuner method, indicating better audio quality.
Quotes
"CONTUNER significantly achieves promising results in both MOS-Q and MOS-E, with audio quality degradation with 0.12, as well as MOS-E being more than those for ground-truth amateur recordings by 1.08 and 1.00 on Mandarin and English data respectively." "As for MOS-E, CONTUNER is less than those for ground-truth professional recordings by only 0.15 and 0.21 in Mandarin and English singing voice data respectively, which proves that the CONTUNER has strong language generalization performance."

Deeper Inquiries

How can CONTUNER's expressiveness enhancement be further improved to better capture the nuances of professional singing?

In order to enhance CONTUNER's expressiveness further and capture the subtleties of professional singing, several strategies can be implemented: Fine-tuning the Expressiveness Enhancer: Continuously training the expressiveness enhancer on a diverse dataset of professional singing voices can help it learn a wider range of expressive variations. By exposing the model to a larger pool of professional performances, it can better understand and replicate the nuances of different singing styles and emotions. Multi-modal Input: Incorporating additional modalities such as lyrics, music scores, or even video footage of professional singers can provide more context for the model to understand the emotional and stylistic elements of a performance. This multi-modal input can enrich the expressiveness representation and lead to more accurate enhancements. Feedback Mechanism: Implementing a feedback loop where the model receives feedback from users or experts on the quality of the expressiveness enhancement can help refine the model over time. By iteratively improving based on feedback, the model can continuously enhance its ability to capture the nuances of professional singing. Transfer Learning: Leveraging transfer learning techniques by pre-training the expressiveness enhancer on a large dataset of professional singing performances before fine-tuning on the specific task at hand can help the model capture a broader range of expressive features.

How could the CONTUNER framework be extended to enable interactive singing voice editing and customization for end-users?

To enable interactive singing voice editing and customization for end-users, the CONTUNER framework can be extended in the following ways: Real-time Feedback: Implement a real-time feedback mechanism where users can listen to the changes made by the model as they adjust parameters like pitch, expressiveness, and other factors. This interactive feedback loop can help users customize the singing voice according to their preferences. User-Friendly Interface: Develop a user-friendly interface that allows users to easily manipulate various parameters such as pitch, tempo, vibrato, and emotion. Providing intuitive controls and visual representations of the changes can enhance the user experience and make the editing process more accessible. Customization Options: Integrate features that enable users to customize specific aspects of the singing voice, such as adjusting the intensity of vibrato, adding personalized effects, or even blending different singing styles. This level of customization can cater to a wide range of user preferences and creative needs. Collaborative Editing: Enable collaborative editing functionalities where multiple users can work together on editing a singing voice in real-time. This feature can be particularly useful for music producers, vocal coaches, or collaborative music projects. By incorporating these extensions, the CONTUNER framework can empower end-users to interactively edit and customize singing voices, fostering creativity and personalization in music production and performance.

What other factors beyond pitch and expressiveness could be incorporated into the beautification process to make the singing voice sound even more natural and professional?

In addition to pitch and expressiveness, several other factors can be incorporated into the beautification process to enhance the naturalness and professionalism of the singing voice: Timbre Adjustment: Including timbre adjustment capabilities in the beautification process can help refine the tonal quality of the singing voice. Users could have the option to modify the brightness, warmth, or richness of the voice to achieve a desired timbral quality. Articulation Control: Allowing users to control the articulation of the singing voice, such as legato, staccato, or phrasing, can add a layer of musicality and realism to the performance. Adjusting articulation can make the singing voice sound more dynamic and expressive. Breath Control: Incorporating breath control parameters can simulate the natural inhalation and exhalation patterns of a singer, adding realism to the performance. Users could adjust breath intensity, timing, and phrasing to mimic a live singing experience. Harmonization and Background Vocals: Offering features for harmonization and adding background vocals can enrich the overall sound of the singing voice. Users could experiment with creating harmonies, layering vocals, or adding choir-like effects to enhance the depth and texture of the performance. By integrating these additional factors into the beautification process, the singing voice can be further refined to sound more natural, professional, and engaging, providing users with a comprehensive toolkit for enhancing their vocal performances.
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