The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 31, 2023

Filed:

Mar. 18, 2021
Applicant:

Spotify Ab, Stockholm, SE;

Inventors:

Andreas Simon Thore Jansson, New York, NY (US);

Angus William Sackfield, Stockholm, SE;

Ching Chuan Sung, Brooklyn, NY (US);

Rachel M. Bittner, Brooklyn, NY (US);

Assignee:

Spotify AB, Stockholm, SE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/00 (2006.01); G10L 25/00 (2013.01); G06N 3/08 (2006.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G10H 1/00 (2006.01); G10L 21/0272 (2013.01); G10L 21/028 (2013.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06K 9/6256 (2013.01); G06N 3/04 (2013.01); G10H 1/0008 (2013.01); G10L 21/028 (2013.01); G10L 21/0272 (2013.01); G10H 2210/056 (2013.01); G10H 2250/311 (2013.01);
Abstract

A system, method and computer product for training a neural network system. The method comprises inputting an audio signal to the system to generate plural outputs f(X, Θ). The audio signal includes one or more of vocal content and/or musical instrument content, and each output f(X, Θ) corresponds to a respective one of the different content types. The method also comprises comparing individual outputs f(X, Θ) of the neural network system to corresponding target signals. For each compared output f(X, Θ), at least one parameter of the system is adjusted to reduce a result of the comparing performed for the output f(X, Θ), to train the system to estimate the different content types. In one example embodiment, the system comprises a U-Net architecture. After training, the system can estimate various different types of vocal and/or instrument components of an audio signal, depending on which type of component(s) the system is trained to estimate.


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