Invention Grant
- Patent Title: Neural network model for generation of compressed haptic actuator signal from audio input
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Application No.: US15949425Application Date: 2018-04-10
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Publication No.: US11355033B2Publication Date: 2022-06-07
- Inventor: Brian Alexander Knott , Venkatasiva Prasad Chakkabala
- Applicant: Meta Platforms, Inc.
- Applicant Address: US CA Menlo Park
- Assignee: Meta Platforms, Inc.
- Current Assignee: Meta Platforms, Inc.
- Current Assignee Address: US CA Menlo Park
- Agency: Fenwick & West LLP
- Main IPC: G09B21/00
- IPC: G09B21/00 ; G01L5/00 ; G06N20/00 ; G06N3/04 ; G06N3/08 ; G10L13/00 ; G10L21/02 ; G08B6/00 ; G09B21/04 ; G10L15/02 ; G10L15/22 ; G10L21/0272 ; G06F3/01 ; G06F3/16 ; G10L25/18 ; G10L25/48 ; G10L19/00 ; G10L21/06 ; G10L15/16

Abstract:
A method comprises inputting an audio signal into a machine learning circuit to compress the audio signal into a sequence of actuator signals. The machine learning circuit being trained by: receiving a training set of acoustic signals and pre-processing the training set of acoustic signals into pre-processed audio data. The pre-processed audio data including at least a spectrogram. The training further includes training the machine learning circuit using the pre-processed audio data. The neural network has a cost function based on a reconstruction error and a plurality of constraints. The machine learning circuit generates a sequence of haptic cues corresponding to the audio input. The sequence of haptic cues is transmitted to a plurality of cutaneous actuators to generate a sequence of haptic outputs.
Public/Granted literature
- US20180300651A1 NEURAL NETWORK MODEL FOR GENERATION OF COMPRESSED HAPTIC ACTUATOR SIGNAL FROM AUDIO INPUT Public/Granted day:2018-10-18
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