Real-time Timbre Remapping with Differentiable DSP

Real-time Timbre Remapping with Differentiable DSP

Overview The paper "Real-time Timbre Remapping with Differentiable DSP" addresses a novel approach to manipulating timbral expression in music, diverging from conventional audio-driven synthesis methods that primarily focus on pitch and loudness. The authors introduce a methodology that prioritizes timbral subtleties by leveraging differentiable digital signal processing (DDSP) to optimize synthesizer parameters via a newly proposed feature difference loss function. This allows for the remapping of timbral expressions from an acoustic source to a synthesizer, highlighted through a case study involving snare drum performances mapped to a differentiable synthesizer modeled after the Roland TR-808.

Introduction Timbre has often been a nuanced and less precisely defined concept in psychoacoustics and music psychology, yet it remains central to various musical traditions. The evolution of sound synthesizers has expanded the timbral palette available to musicians, enabling new genres and forms of expression. Traditional neural network-based timbre transfer methods typically prioritize pitch and loudness control, implicitly learning timbre. This paper disrupts this norm by focusing on the explicit utilization of timbre as a control signal.

Methodology The primary innovation lies in the feature difference loss function, which measures the relative differences in audio features rather than their absolute values. This loss function is designed to optimize synthesizer parameters to match these relative timbral differences, facilitating the remapping of timbres in real-time musical contexts. The method uses an adjustable differentiable synthesizer and gradient-based optimization to achieve this.

For the case study, the authors implemented a differentiable synthesizer inspired by the Roland TR-808 snare drum. This synthesizer consists of sinusoidal oscillators and a noise generator with fourteen synthesis parameters. The audio features, derived from prior research, include dynamic and timbral descriptors such as sound pressure level, temporal centroid, spectral centroid, and spectral flatness. These features are processed through frame-based and psychoacoustically scaled computations to ensure perceptual relevance.

Experiments and Results The experiments involved training models to map onset features from snare drum hits to synthesizer parameter modulations. These models, including linear and multi-layer perceptron (MLP) variations, demonstrated promising results in matching feature differences.

The numerical results showed that direct optimization of parameters provided the best feature difference alignment, yet the MLP models also performed adequately. This indicates that the feature difference loss function effectively guides the model in learning parametric adjustments that reflect relative timbral changes.

An informal musical experiment with a professional drummer provided qualitative validation. The participant noted the subtle reactiveness of the synthesized sounds, which sustained the perception of continuous timbral variation—an intended outcome of the proposed method. The deployment of the timbre remapping system as a real-time audio plugin enabled practical evaluation within a typical workflow, highlighting areas for future improvement, particularly in onset detection latency and feature normalization.

Implications and Future Developments The approach presented has significant implications for both theoretical understanding and practical applications. The explicit use of timbral features as control signals enriches the interface design for sound synthesizers, potentially offering a new dimension of expressive control. The real-time applicability makes it suitable for live performances and interactive music systems, enhancing the gestural language of performers.

Future research could explore psychoacoustical validation of the feature difference loss function, extend the concept to more complex and diverse musical contexts, and refine onset detection mechanisms to reduce latency. Additionally, integrating more advanced machine learning models or techniques could further improve the accuracy and subtlety of timbre mapping.

Conclusion "Real-time Timbre Remapping with Differentiable DSP" contributes a novel methodological framework to the field of sound synthesis, focusing on the nuanced control of timbre. By prioritizing relative timbral differences and optimizing synthesizer parameters in a differentiable context, the work provides new opportunities for expressive musical interaction and sound design. The case study validates the practical viability of this approach, with promising results that warrant further exploration and refinement.