Differentiable Digital Signal Processing

Differentiable digital signal processing (DDSP) integrates traditional signal processing techniques with deep learning, enabling the end-to-end training of audio and signal processing models. Current research focuses on developing DDSP-based models for various applications, including audio synthesis (e.g., using harmonic plus noise models and autoencoders), noise reduction, and effects processing, often leveraging architectures like autoencoders and generative adversarial networks. This approach offers advantages in terms of computational efficiency, interpretability, and controllability compared to purely black-box neural network methods, impacting fields ranging from music production and speech synthesis to cybersecurity and robotics.