US 12,334,095 B2
Meta-learning for adaptive filters
Nicholas J. Bryan, Belmont, CA (US); and Paris Smaragdis, Urbana, IL (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Jan. 17, 2023, as Appl. No. 18/155,611.
Claims priority of provisional application 63/332,992, filed on Apr. 20, 2022.
Prior Publication US 2023/0343350 A1, Oct. 26, 2023
Int. Cl. G10L 21/0232 (2013.01); G10L 25/18 (2013.01); G10L 25/30 (2013.01); G10L 21/0208 (2013.01); G10L 21/0224 (2013.01)
CPC G10L 21/0232 (2013.01) [G10L 25/18 (2013.01); G10L 25/30 (2013.01); G10L 2021/02082 (2013.01); G10L 21/0224 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by a filter of an adaptive filter system, an input audio signal, wherein the input audio signal is a far-end audio signal, the filter including a transfer function with adaptable filter weights for modeling an acoustic environment;
generating, by the filter, a response audio signal, the response audio signal modeling the input audio signal passing through the acoustic environment;
receiving a target response signal produced from the input audio signal passing through the acoustic environment, the target response signal including the input audio signal and near-end audio signals;
calculating an adaptive filter loss using the response audio signal and the target response signal;
generating, by a trained recurrent neural network of the adaptive filter system, a filter weight update using the calculated adaptive filter loss;
updating the adaptable filter weights of the transfer function using the filter weight update to create an updated transfer function;
generating, by the filter, an updated response audio signal based on the updated transfer function; and
providing the updated response audio signal as an output audio signal.