US Patent Application 18344567. Machine-Learned Differentiable Digital Signal Processing simplified abstract
Contents
Machine-Learned Differentiable Digital Signal Processing
Organization Name
Inventor(s)
Jesse Engel of Oakland CA (US)
Adam Roberts of Oakland CA (US)
Chenjie Gu of Sunnyvale CA (US)
Lamtharn Hantrakul of San Francisco CA (US)
Machine-Learned Differentiable Digital Signal Processing - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 18344567 Titled 'Machine-Learned Differentiable Digital Signal Processing'
Simplified Explanation
The abstract describes a new approach to digital signal processing using machine-learned differentiable digital signal processors. This involves incorporating these processors into the training process of a machine learning model, allowing for more efficient and high-quality signal processing. This approach can lead to smaller models, resulting in reduced energy costs for storage and processing of digital signals.
Original Abstract Submitted
Systems and methods of the present disclosure are directed toward digital signal processing using machine-learned differentiable digital signal processors. For example, embodiments of the present disclosure may include differentiable digital signal processors within the training loop of a machine-learned model (e.g., for gradient-based training). Advantageously, systems and methods of the present disclosure provide high quality signal processing using smaller models than prior systems, thereby reducing energy costs (e.g., storage and/or processing costs) associated with performing digital signal processing.
