DDSP

Provides core synthesizers, filters, and effects with automatic differentiation, enabling gradient-based optimization in neural networks for audio generation, as highlighted in the library's description.

Processors format neural network outputs into physically meaningful controls like amplitudes and frequencies, allowing interpretable and steerable audio synthesis, demonstrated in the Processor examples.

Uses Gin dependency injection to define processor graphs without code changes, offering high reconfigurability for experiments, as shown in the ProcessorGroup section with gin.

Includes loss functions and models for tasks like pitch detection and timbre transfer, supporting research in audio representation learning, as mentioned in the demos and key features.