US 12,217,742 B2
High fidelity audio super resolution
Zeyu Jin, San Francisco, CA (US); Jiaqi Su, Princeton, NJ (US); and Adam Finkelstein, Princeton, NJ (US)
Assigned to Adobe Inc., San Jose, CA (US); and The Trustees of Princeton University, Princeton, NJ (US)
Filed by Adobe Inc., San Jose, CA (US); and The Trustees of Princeton University, Princeton, NJ (US)
Filed on Nov. 23, 2021, as Appl. No. 17/534,221.
Prior Publication US 2023/0162725 A1, May 25, 2023
Int. Cl. G10L 15/16 (2006.01); G06N 3/045 (2023.01); G10L 15/06 (2013.01)
CPC G10L 15/16 (2013.01) [G06N 3/045 (2023.01); G10L 15/063 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving narrow-band input audio data;
dividing the narrow-band input audio data into a plurality of batches;
upsampling each batch of the narrow-band input audio data to generate upsampled audio batch data;
providing the upsampled audio batch data to an audio super resolution model, wherein the audio super resolution model is a generator neural network trained as part of a generative adversarial network (GAN) to generate wide-band audio data from narrow-band audio data;
generating, by the audio super resolution model, wide-band audio batch data corresponding to each batch of the plurality of batches, the wide-band audio batch data including high frequency data not included in the narrow-band input audio data;
combining the wide-band audio batch data corresponding to each batch of the plurality of batches to form wide-band output audio data; and
returning the wide-band output audio data corresponding to the narrow-band input audio data.