IP Library › Granted Patent US 11,276,413
Granted Patent B2
US 11,276,413 · App. 16/543,095 · Granted Mar 15, 2022

Audio signal encoding method and audio signal decoding method, and encoder and decoder performing the same

Inventors: Mi Suk Lee (Daejeon, KR); Jongmo Sung (Daejeon, KR); Minje Kim (Bloomington, IN); Kai Zhen (Bloomington, IN)
Assignees: Electronics and Telecommunications Research Institute; THE TRUSTEES OF INDIANA UNIVERSITY
G10L19/167
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,276,413
App. No.
16/543,095
Granted
Mar 15, 2022
Kind
B2
Abstract

Disclosed are an audio signal encoding method and audio signal decoding method, and an encoder and decoder performing the same. The audio signal encoding method includes applying an audio signal to a training model including N autoencoders provided in a cascade structure, encoding an output result derived through the training model, and generating a bitstream with respect to the audio signal based on the encoded output result.

Claims (24)

1. An audio signal encoding method, comprising:

applying an audio signal to a training model including N autoencoders provided in a cascade structure such that the N autoencoders are each connected in series;

encoding an output result derived through the training model; and

generating a bitstream with respect to the audio signal based on the encoded output result,

wherein the training model is derived by connecting the N autoencoders in a cascade form, and training a subsequent autoencoder using a residual signal not learned by a previous autoencoder,

wherein a residual signal of the previous autoencoder is an input of the subsequent autoencoder.

2. The audio signal encoding method of claim 1 , wherein the training model is derived by iteratively updating autoencoders provided in a cascade form through M update rounds.

3. The audio signal encoding method of claim 1 , wherein the training model is a model that an error of an N-th autoencoder is back propagated respectively to a first autoencoder through an (N−1)-th autoencoder.

4. The audio signal encoding method of claim 1 , wherein the training model is a model that respective errors of the N autoencoders are back propagated from respective decoder regions to encoder regions.

5. An audio signal decoding method, comprising:

restoring a code layer parameter from a bitstream;

applying the restored code layer parameter to a training model including N autoencoders provided in a cascade structure such that the N autoencoders are each connected in series; and

restoring an audio signal before encoding through the training model,

wherein the training model is derived by connecting the N autoencoders in a cascade form, and training a subsequent autoencoder using a residual signal not learned by a previous autoencoder,

wherein a residual signal of the previous autoencoder is an input of the subsequent autoencoder.

6. The audio signal decoding method of claim 5 , wherein the training model is derived by iteratively updating autoencoders provided in a cascade form through M update rounds.

7. The audio signal decoding method of claim 6 , wherein the training model is a model that an error of an N-th autoencoder is back propagated respectively to a first autoencoder through an (N−1)-th autoencoder.

8. The audio signal decoding method of claim 6 , wherein the training model is a model that respective errors of the N autoencoders are back propagated from decoder regions to encoder regions.

9. An audio signal decoder, comprising:

a processor configured to restore a code layer parameter from a bitstream, apply the restored code layer parameter to a training model including N autoencoders provided in a cascade structure such that the N autoencoders are each connected in series, and restore an audio signal before encoding through the training model,

wherein the training model is derived by connecting the N autoencoders in a cascade form, and training a subsequent autoencoder using a residual signal not learned by a previous autoencoder.

10. The audio signal decoder of claim 9 , wherein the training model is derived by iteratively updating autoencoders provided in a cascade form through M update rounds.

11. The audio signal decoder of claim 10 , wherein the training model is a model that an error of an N-th autoencoder is back propagated respectively to a first autoencoder through an (N−1)-th autoencoder.

12. The audio signal decoder of claim 9 , wherein the training model is a model that respective errors of the N autoencoders are back propagated from decoder regions to encoder regions.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 16, 2019
From: LEE, MI SUK; SUNG, JONGMO; KIM, MINJE; ZHEN, KAI
To: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE; THE TRUSTEES OF INDIANA UNIVERSITY
Reel/Frame 050077/0876 →
Priority Claims (1)
KR 10-2019-0022612 · Feb 26, 2019 · national
Continuity (2)
Provisional Application 62751105 · Oct 26, 2018
Related Publication 20200135220A1 · Apr 30, 2020