Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
112 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
3 tokens/sec
DeepSeek R1 via Azure Pro
55 tokens/sec
2000 character limit reached

RDSinger: Reference-based Diffusion Network for Singing Voice Synthesis (2410.21641v1)

Published 29 Oct 2024 in cs.SD, cs.AI, and eess.AS

Abstract: Singing voice synthesis (SVS) aims to produce high-fidelity singing audio from music scores, requiring a detailed understanding of notes, pitch, and duration, unlike text-to-speech tasks. Although diffusion models have shown exceptional performance in various generative tasks like image and video creation, their application in SVS is hindered by time complexity and the challenge of capturing acoustic features, particularly during pitch transitions. Some networks learn from the prior distribution and use the compressed latent state as a better start in the diffusion model, but the denoising step doesn't consistently improve quality over the entire duration. We introduce RDSinger, a reference-based denoising diffusion network that generates high-quality audio for SVS tasks. Our approach is inspired by Animate Anyone, a diffusion image network that maintains intricate appearance features from reference images. RDSinger utilizes FastSpeech2 mel-spectrogram as a reference to mitigate denoising step artifacts. Additionally, existing models could be influenced by misleading information on the compressed latent state during pitch transitions. We address this issue by applying Gaussian blur on partial reference mel-spectrogram and adjusting loss weights in these regions. Extensive ablation studies demonstrate the efficiency of our method. Evaluations on OpenCpop, a Chinese singing dataset, show that RDSinger outperforms current state-of-the-art SVS methods in performance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 22563–22575.
  2. Real time speech enhancement in the waveform domain. arXiv preprint arXiv:2006.12847.
  3. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34: 8780–8794.
  4. Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32.
  5. Generative adversarial networks. Communications of the ACM, 63(11): 139–144.
  6. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725.
  7. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33: 6840–6851.
  8. Hu, L. 2024. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8153–8163.
  9. Hiddensinger: High-quality singing voice synthesis via neural audio codec and latent diffusion models. arXiv preprint arXiv:2306.06814.
  10. Introducing Parselmouth: A Python interface to Praat. Journal of Phonetics, 71: 1–15.
  11. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. In International Conference on Machine Learning, 5530–5540. PMLR.
  12. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in neural information processing systems, 33: 17022–17033.
  13. Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761.
  14. Diffsinger: Singing voice synthesis via shallow diffusion mechanism. In Proceedings of the AAAI conference on artificial intelligence, volume 36, 11020–11028.
  15. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499.
  16. Wave-GAN: a deep learning approach for the prediction of nonlinear regular wave loads and run-up on a fixed cylinder. Coastal Engineering, 167: 103902.
  17. Grad-tts: A diffusion probabilistic model for text-to-speech. In International Conference on Machine Learning, 8599–8608. PMLR.
  18. Waveglow: A flow-based generative network for speech synthesis. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3617–3621. IEEE.
  19. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2): 3.
  20. DNSMOS P. 835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 886–890. IEEE.
  21. Fastspeech 2: Fast and high-quality end-to-end text to speech. arXiv preprint arXiv:2006.04558.
  22. Deepsinger: Singing voice synthesis with data mined from the web. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1979–1989.
  23. Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 01CH37221), volume 2, 749–752. IEEE.
  24. A hierarchical latent vector model for learning long-term structure in music. In International conference on machine learning, 4364–4373. PMLR.
  25. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 10684–10695.
  26. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), 4779–4783. IEEE.
  27. An algorithm for intelligibility prediction of time–frequency weighted noisy speech. IEEE Transactions on audio, speech, and language processing, 19(7): 2125–2136.
  28. Opencpop: A high-quality open source chinese popular song corpus for singing voice synthesis. arXiv preprint arXiv:2201.07429.
  29. ESPnet: End-to-end speech processing toolkit. arXiv preprint arXiv:1804.00015.
  30. Adversarially trained multi-singer sequence-to-sequence singing synthesizer. arXiv preprint arXiv:2006.10317.
  31. Dynamic Sliding Window for Realtime Denoising Networks. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 361–365. IEEE.
  32. Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6199–6203. IEEE.
  33. Visinger: Variational inference with adversarial learning for end-to-end singing voice synthesis. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7237–7241. IEEE.

Summary

  • The paper introduces a reference-based diffusion network that improves singing voice synthesis by accurately reproducing pitch transitions and temporal dynamics.
  • It combines a FastSpeech2-generated mel-spectrogram with Gaussian blur in transition regions to minimize artifacts and enhance acoustic fidelity.
  • Experimental results on the OpenCpop dataset show superior performance in MOS, SIG MOS, and BAK MOS compared to traditional models like DiffSinger.

Reference-based Diffusion Network for Singing Voice Synthesis: An Examination of RDSinger

The paper presents RDSinger, a novel diffusion-based network architecture aimed at addressing the challenges inherent in Singing Voice Synthesis (SVS). Unlike text-to-speech systems that primarily focus on converting text to natural-sounding speech, SVS requires an accurate reproduction of pitch, notes, and durations, making it considerably more complex. The authors propose a reference-based diffusion model inspired by techniques from image generation, adapted for enhancing the quality and fidelity of synthesized singing voice audio.

Overview of RDSinger

RDSinger leverages a two-part structure comprising a reference network in conjunction with a denoising diffusion network. The process begins with the generation of a mel-spectrogram using FastSpeech2, which serves as a reference input for the model. Notably, the paper explores overcoming the challenge posed by diffusion models in maintaining consistency, particularly in pitch transitions, a known pain point in generating singing voice audio. Existing models, such as DiffSinger, employ shallow diffusion networks to expedite inference but struggle with maintaining acoustic authenticity during note and pitch transitions.

To address this critical issue, RDSinger incorporates elements distinct from prior models:

  1. Reference-Based Diffusion Network: Embracing the concept from the Animate Anyone framework, RDSinger utilizes a reference mel-spectrogram to guide the diffusion network, leading to more refined audio generation.
  2. Gaussian Blur for Transition Regions: The approach involves applying Gaussian blur to identified transition regions, curbing artifacts that often emerge due to misleading information from the intermediate mel-spectrogram. This is coupled with targeted loss weighting adjustments to accentuate the learning of crucial transition areas.
  3. Enhanced Architectural Design: The integration of FastSpeech2 with the reference network aids in preserving pitch and duration fidelity while concurrently enriching the denoising process, resulting in a more natural reproduction of the singing voice.

Experimental Validation

The authors conducted experiments on the OpenCpop dataset, exploring variations with different SVS models. RDSinger demonstrated superior performance, eclipsing state-of-the-art methods in metrics such as mean opinion score (MOS), signal quality measures (SIG MOS), and background suppression metrics (BAK MOS). The ablation studies further shed light on the model's nuanced design, establishing the significance of each component: the referencing mechanism, the Gaussian blur application in pitch transitions, and the modulation of loss weights. Notably, RDSinger achieved optimal outcomes with 100 denoising steps, affirming the efficiency of its diffusion process vis-à-vis traditional models like DiffSinger.

Implications and Future Directions

RDSinger's innovations herald substantial implications for the field of SVS, advancing both the methodology and practical applications of voice synthesis technologies. The reference-based diffusion approach underscores an enhanced ability to preserve detail and minimize artifacts, potentially influencing future designs in areas such as real-time music production and virtual voice applications. The introduction of strategic processing mechanisms such as region-specific Gaussian blur and targeted loss weighting indicates new horizons for improving audio synthesis quality.

Looking forward, there is promising potential for further exploration of diffusion models within the broader domain of audio synthesis. Enhancing the computational efficiency of such models without compromising output fidelity remains a key area of interest, as does the exploration of multi-modal synthesis applications that integrate both speech and singing capabilities. Additionally, the methodologies proposed can be extended to diverse linguistic and musical contexts, requiring further validation across varied datasets and languages.

In conclusion, the paper offers a noteworthy contribution to SVS research by presenting a methodologically sound and empirically validated diffusion-based network. RDSinger stands as a compelling step forward, fostering continued advancements and refinements in synthesized singing voice technologies.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now