Advanced Conditional Frameworks for Probabilistic Sound Generation Enabling Greater Authenticity and Tonal Regulation
Keywords:
Probabilistic Sound Generation, Diffusion Models, Conditional Generative ModelsAbstract
Probabilistic sound generation has undergone a transformative evolution with the emergence of deep generative models, particularly diffusion-based architectures, variational autoencoders, and generative adversarial networks. While these approaches have significantly improved the realism of synthesized audio, they often suffer from limitations in controllability and tonal precision. This paper investigates advanced conditional frameworks designed to enhance both authenticity and fine-grained acoustic regulation in generative audio systems. By synthesizing insights from foundational generative modeling techniques and recent advancements in diffusion-based sound synthesis, this study proposes a structured analytical perspective on multi-condition integration strategies.
The research explores how conditioning mechanisms—such as textual prompts, spectral features, symbolic representations, and performance parameters—affect the probabilistic modeling of sound. It further evaluates the interplay between conditioning modalities and generative architectures, highlighting how diffusion models enable iterative refinement processes that align outputs with desired tonal characteristics. Theoretical grounding is provided through probabilistic modeling frameworks, including latent variable models and score-based generative processes, enabling a deeper understanding of how conditional signals influence output distributions.
A critical component of the study is the comparative analysis of conditioning strategies across architectures, including waveform-based synthesis (e.g., WaveNet), spectrogram-based modeling, and latent diffusion systems. The paper identifies key challenges such as mode collapse, over-conditioning, and loss of diversity, and examines mitigation strategies through hierarchical conditioning and adaptive weighting schemes. Additionally, evaluation metrics such as Fréchet Audio Distance and perceptual realism measures are analyzed to assess improvements in generated audio quality.
The findings suggest that advanced conditional frameworks significantly enhance both perceptual realism and controllability, particularly when multi-modal conditioning is incorporated. However, trade-offs emerge between flexibility and computational complexity, necessitating optimized architectures. This work contributes to the field by offering a comprehensive framework for understanding conditional sound generation and outlining future directions for scalable, interpretable, and high-fidelity audio synthesis systems.
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References
Agostinelli, “MusicLM: Generating music from text,” 2023, arXiv:2301.11325.
H. Dong, C. Zhou, T. Berg-Kirkpatrick, and J. J. McAuley, “Deep performer: Score-to-audio music performance synthesis,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Singapore, 2022 pp. 951–955.
J. H. Engel, “Neural audio synthesis of musical notes with wavenet autoencoders,” in Proc. Int. Conf. Mach. Learn., Sydney, Australia, 2017, vol. 70, pp. 1068–1077.
J. Goodfellow, “Generative adversarial networks,” Commun. ACM, vol. 63, pp. 139–144, 2020.
Hawthorne, “Multi-instrument music synthesis with spectrogram diffusion,” in Proc. Int. Soc. Music Inf. Retrieval Conf., 2022, pp. 598–607.
Hawthorne, “Enabling factorized piano music modeling and generation with the MAESTRO dataset,” in Proc. Int. Conf. Learn. Representations, New Orleans, Louisiana, USA, 2019.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 6840–6851.
Q. Huang, “Noise2Music: Text-conditioned music generation with diffusion models,” 2023, arXiv:2302.03917.
K. Karplus and A. Strong, “Digital synthesis of plucked-string and drum timbres,” Comput. Music J., vol. 7, no. 2, pp. 43–55, 1983.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2019, pp. 4401–4410.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proc. 2020 IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Seattle, WA, USA, 2020, pp. 8107–8116.
M. Jeong, H. Kim, S. J. Cheon, B. J. Choi, and N. S. Kim, “Diff-TTS: A denoising diffusion model for text-to-speech,” in Proc. 22nd Annu. Conf. Int. Speech Commun. Assoc., Brno, Czechia, 2021, pp. 3605–3609.
J. W. Kim, R. M. Bittner, A. Kumar, and J. P. Bello, “Neural music synthesis for flexible timbre control,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Brighton, U.K., 2019, pp. 176–180.
P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proc. 2nd Int. Conf. Learn. Representations, Y. Bengio and Y. LeCun, Eds. Banff, AB, Canada, Apr. 2014. [Online]. Available: http://arxiv.org/abs/1312.6114
K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fréchet audio distance: A reference-free metric for evaluating music enhancement algorithms,” in Proc. Annu. Conf. Int. Speech Commun. Assoc., Graz, Austria, 2019, pp. 2350–2354.
H. Kim, S. Choi, and J. Nam, “Expressive acoustic guitar sound synthesis with an instrument-specific input representation and diffusion outpainting,” in Proc. 2024 IEEE Int. Conf. Acoust., Speech Signal Process., 2024, pp. 7620–7624.
B. Maman, J. Zeitler, M. Müller, and A. H. Bermano, “Performance conditioning for diffusion-based multi-instrument music synthesis,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Seoul, South Korea, 2024, pp. 5045–5049.
B. Maman and A. H. Bermano, “Unaligned supervision for automatic music transcription in the wild,” in Proc. Int. Conf. Mach. Learn., Baltimore, Maryland, USA, 2022, pp. 14918–14934.
Maestre, R. Ramírez, S. Kersten, and X. Serra, “Expressive concatenative synthesis by reusing samples from real performance recordings,” Comput. Music J., vol. 33, no. 4, pp. 23–42, 2009.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., New Orleans, USA, 2022, pp. 10674–10685.
Radford, “Learning transferable visual models from natural language supervision,” in Proc. Int. Conf. Mach. Learn., 2021, pp. 8748–8763.
Saharia, “Photorealistic text-to-image diffusion models with deep language understanding,” in Proc. Adv. Neural Inf. Process. Syst. 35: Annu. Conf. Neural Inf. Process. Syst., New Orleans, LA, USA, 2022, pp. 36479–36494.
Schneider, Z. Jin, and B. Schölkopf, “Moûsai: Text-to-music generation with long-context latent diffusion,” 2023, arXiv:2301.11757.
Schwarz, “Current research in concatenative sound synthesis,” in Proc. Int. Comput. Music Conf., Barcelona, Spain, Sep. 2005.
Schwarz, “Concatenative sound synthesis: The early years,” J. New Music Reaserch, vol. 35, no. 1, pp. 3–22, Mar. 2006.
J. O. Smith, “Physical modeling using digital waveguides,” Comput. Music J., vol. 16, no. 4, pp. 74–91, 1992.
B. L. Sturm, “Adaptive concatenative sound synthesis and its application to micromontage composition,” Comput. Music J., vol. 30, no. 4, pp. 46–66, 2006.
J. Tseng, R. Castellon, and C. K. Liu, “Edge: Editable dance generation from music,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 448–458.
van den Oord, “WaveNet: A generative model for raw audio,” in Proc. ISCA Speech Synth. Workshop, Sunnyvale, USA, 2016.
Wang and Y. Yang, “Performancenet: Score-to-audio music generation with multi-band convolutional residual network,” in Proc. Conf. Artif. Intell., Honolulu, Hawaii, 2019, pp. 1174–1181.
Y. Wu, “MIDI-DDSP: Detailed control of musical performance via hierarchical modeling,” in Proc. Int. Conf. Learn. Representations, 2022.
N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi, “Soundstream: An end-to-end neural audio codec,” IEEE/ACM Trans. Audio, Speech Lang. Process., vol. 30, pp. 495–507, 2022.
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