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DiffMoog: a Differentiable Modular Synthesizer for Sound Matching (2401.12570v1)

Published 23 Jan 2024 in eess.AS, cs.AI, and cs.SD

Abstract: This paper presents DiffMoog - a differentiable modular synthesizer with a comprehensive set of modules typically found in commercial instruments. Being differentiable, it allows integration into neural networks, enabling automated sound matching, to replicate a given audio input. Notably, DiffMoog facilitates modulation capabilities (FM/AM), low-frequency oscillators (LFOs), filters, envelope shapers, and the ability for users to create custom signal chains. We introduce an open-source platform that comprises DiffMoog and an end-to-end sound matching framework. This framework utilizes a novel signal-chain loss and an encoder network that self-programs its outputs to predict DiffMoogs parameters based on the user-defined modular architecture. Moreover, we provide insights and lessons learned towards sound matching using differentiable synthesis. Combining robust sound capabilities with a holistic platform, DiffMoog stands as a premier asset for expediting research in audio synthesis and machine learning.

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Citations (3)
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Summary

  • The paper presents DiffMoog, a synthesizer that integrates differentiable operations to optimize sound matching using gradient-based learning.
  • It leverages a novel signal-chain loss and Wasserstein frequency loss to enhance synthetic sound fidelity and parameter inference.
  • The open-source framework supports unsupervised learning, fostering innovation in AI-driven audio synthesis research.

DiffMoog: A Differentiable Modular Synthesizer for Sound Matching

The paper discusses DiffMoog, a modular synthesizer embedded with differentiable capabilities, designed to advance research in sound synthesis and sound matching. Developed by a team of researchers at Tel Aviv University, DiffMoog is distinguished by its integration into machine learning frameworks, addressing the constraints of non-differentiable synthesizers in AI-based audio design. This modular synthesizer offers a sophisticated array of sound-manipulating modules—like FM and AM oscillators, LFOs, filters, and envelope shapers—allowing it to align closely with the architecture of commercial synthesizers.

Key Concepts and Contributions

DiffMoog represents an advance in differentiable synthesis by simulating the signal processing capabilities typical of commercial synthesizers, while remaining optimized for gradient-based computations used in neural networks. In contrast to earlier differentiable synthesizers that either oversimplified or overcomplicated the model structures, DiffMoog ensures both high fidelity in synthetic sound reproduction and the modularity necessary for creating customized signal chains. The open-source platform introduced alongside DiffMoog is equipped with an end-to-end sound matching framework, a novel signal-chain loss function, and an encoder network predicting synthesizer parameters from audio inputs.

The framework is especially effective as it facilitates unsupervised learning, which allows researchers to replicate unlabeled sounds not initially generated by the synthesizer. The publication denotes the potential of DiffMoog in expediting research in audio synthesis and AI, facilitated through its flexible structure and compatibility with conventional sound synthesis paradigms.

Numerical Results and Evaluations

The paper demonstrates engaging results when applying a newly introduced signal-chain loss for optimizing sound matching, especially for synthesizers configured with fundamental chains containing oscillators, ADSR envelopes, and filters. However, training with signal-chain loss remains challenging due to the complexity of optimizing frequency and modulation parameters, which often result in non-convergence. Noteworthy is the finding that Wasserstein loss, applied to frequency estimations, significantly improves accuracy compared to other spectral loss configurations.

Implications and Future Directions

The implementation of DiffMoog carries both theoretical and practical implications in differentiable synthesizer design and machine learning. The modularity and differentiable nature of DiffMoog lower the entry barriers for AI-led sound synthesis, fostering new directions for future research in unsupervised sound matching and synthesis parameter inference.

Despite the promising capabilities of DiffMoog, synthesizers utilizing more advanced FM modulations could not achieve stable convergence, indicating an area necessitating further investigation. The authors speculate that enhanced loss functions, sophisticated optimization methods, and novel neural network structures could furnish plausible solutions to these limitations. This hints at a broader window of opportunities for improvements that may drive future updates and inspire subsequent research within the domain of AI-driven audio synthesis.

By making DiffMoog open-source, the authors provide a significant contribution to the research community. As it stands, DiffMoog could be an invaluable tool for researchers aiming to explore the frontiers of AI-enhanced modulation and sound reproduction, offering a framework within which the interplay of audio signal processing and deep learning can be further examined and expanded upon.

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