公开(公告)号：US12080311B2

公开(公告)日：2024-09-03

申请号：US18344567

申请日：2023-06-29

Applicant: Google LLC

Inventor： Jesse Engel ,  Adam Roberts ,  Chenjie Gu ,  Lamtharn Hantrakul

IPC: G10L19/26 ,  G06N3/084 ,  G06N20/00 ,  G10L25/30

CPC classification number: G10L19/26 ,  G06N3/084 ,  G06N20/00 ,  G10L25/30

Abstract: 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.

公开(公告)号：US20230343348A1

公开(公告)日：2023-10-26

申请号：US18344567

申请日：2023-06-29

Applicant: Google LLC

Inventor： Jesse Engel ,  Adam Roberts ,  Chenjie Gu ,  Lamtharn Hantrakul

IPC: G10L19/26 ,  G10L25/30 ,  G06N20/00 ,  G06N3/084

CPC classification number: G10L19/26 ,  G10L25/30 ,  G06N20/00 ,  G06N3/084

Abstract: 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.

公开(公告)号：US20240395277A1

公开(公告)日：2024-11-28

申请号：US18792298

申请日：2024-08-01

Applicant: Google LLC

Inventor： Jesse Engel ,  Adam Roberts ,  Chenjie Gu ,  Lamtharn Hantrakul

IPC: G10L19/26 ,  G06N3/084 ,  G06N20/00 ,  G10L25/30

Abstract: 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.

公开(公告)号：US11735197B2

公开(公告)日：2023-08-22

申请号：US16922543

申请日：2020-07-07

Applicant: Google LLC

Inventor： Jesse Engel ,  Adam Roberts ,  Chenjie Gu ,  Lamtharn Hantrakul

IPC: G10L25/30 ,  G10L19/26 ,  G06N20/00 ,  G06N3/084

CPC classification number: G10L19/26 ,  G06N3/084 ,  G06N20/00 ,  G10L25/30

Abstract: 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.

公开(公告)号：US20220013132A1

公开(公告)日：2022-01-13

申请号：US16922543

申请日：2020-07-07

Applicant: Google LLC

Inventor： Jesse Engel ,  Adam Roberts ,  Chenjie Gu ,  Lamtharn Hantrakul

IPC: G10L19/26 ,  G06N3/08 ,  G06N20/00 ,  G10L25/30

Abstract: 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.