Methods and apparatus to adjust audio playback settings based on analysis of audio characteristics

Patent metadata

Field	Value
Publication number	US-11218125-B2
Application number	US-201916661973-A
Country	US
Kind code	B2
Filing date	Oct 23, 2019
Priority date	Oct 24, 2018
Publication date	Jan 4, 2022
Grant date	Jan 4, 2022

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings based on analysis of audio characteristics. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal; an EQ filter settings analyzer to: access a plurality of audio playback settings determined by the neural network based on the query; and determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and an EQ adjustment implementer to apply the filter coefficient to the audio signal in a first duration.

First claim

What is claimed is:

1. An apparatus comprising:
an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal, the neural network trained to mitigate different preferences associated with respective audio playback settings determined by at least two audio engineers that tailored the respective audio playback settings for a library of reference audio signals used to train the neural network;
an EQ filter settings analyzer to:
access a plurality of audio playback settings determined by the neural network based on the query; and
determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and

an EQ adjustment implementor to apply the filter coefficient to the audio signal in a first duration.

2. The apparatus of claim 1 , wherein the representation of the sample of the audio signal corresponds to a constant-Q transform representation of the sample of the audio signal.

3. The apparatus of claim 1 , wherein the plurality of audio playback settings include filter settings associated with one or more filters, wherein the filter settings include one or more respective gain values, respective frequency values, or respective quality factor values associated with the sample of the audio signal.

4. The apparatus of claim 1 , wherein the EQ filter settings analyzer is to determine the filter coefficient to apply to the audio signal based on a type of a filter associated with the filter coefficient to be applied to the audio signal.

5. The apparatus of claim 1 , wherein the EQ adjustment implementor is to apply a smoothing filter to the audio signal to reduce sharp transitions in average gain values of the audio signal between the first duration and a second duration.

6. The apparatus of claim 1 , further including a signal transformer to transform the audio signal to a frequency representation of the sample of the audio signal.

7. The apparatus of claim 1 , wherein the EQ adjustment implementor is to adjust at least one of an amplitude characteristic, a frequency characteristic, or a phase characteristic of the audio signal based on the filter coefficient.

8. A non-transitory computer readable storage medium comprising instructions which, when executed, cause one or more processors to at least:
generate a query to a neural network, the query including a representation of a sample of an audio signal, the neural network trained to mitigate different preferences associated with respective audio playback settings determined by at least two audio engineers that tailored the respective audio playback settings for a library of reference audio signals used to train the neural network;
access a plurality of audio playback settings determined by the neural network based on the query;
determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and
apply the filter coefficient to the audio signal in a first duration.

9. The non-transitory computer readable storage medium of claim 8 , wherein the representation of the sample of the audio signal corresponds to a constant-Q transform representation of the sample of the audio signal.

10. The non-transitory computer readable storage medium of claim 8 , wherein the plurality of audio playback settings include filter settings associated with one or more filters, wherein the filter settings include one or more respective gain values, respective frequency values, or respective quality factor values associated with the sample of the audio signal.

11. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the one or more processors to determine the filter coefficient to apply to the audio signal based on a type of a filter associated with the filter coefficient to be applied to the audio signal.

12. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the one or more processors to apply a smoothing filter to the audio signal to reduce sharp transitions in average gain values of the audio signal between the first duration and a second duration.

13. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the one or more processors to transform the audio signal to a frequency representation of the sample of the audio signal.

14. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the one or more processors to adjust at least one of an amplitude characteristic, a frequency characteristic, or a phase characteristic of the audio signal based on the filter coefficient.

15. A method comprising:
generating a query to a neural network, the query including a representation of a sample of an audio signal, the neural network trained to mitigate different preferences associated with respective audio playback settings determined by at least two audio engineers that tailored the respective audio playback settings for a library of reference signals used to train the neural network;
accessing a plurality of audio playback settings determined by the neural network based on the query;
determining a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and
applying the filter coefficient to the audio signal in a first duration.

16. The method of claim 15 , wherein the representation of the sample of the audio signal corresponds to a constant-Q transform representation of the sample of the audio signal.

17. The method of claim 15 , wherein the plurality of audio playback settings include filter settings associated with one or more filters, wherein the filter settings include one or more respective gain values, respective frequency values, or respective quality factor values associated with the sample of the audio signal.

18. The method of claim 15 , further including determining the filter coefficient to apply to the audio signal based on a type of a filter associated with the filter coefficient to be applied to the audio signal.

19. The method of claim 15 , further including applying a smoothing filter to the audio signal to reduce sharp transitions in average gain values of the audio signal between the first duration and a second duration.

20. The method of claim 15 , further including transforming the audio signal to a frequency representation of the sample of the audio signal.

Assignees

• Gracenote Inc

Inventors

• Coover Robert
• Summers Cameron Aubrey
• Hodges Todd
• Renner Joseph
• Cremer Markus
• Mccallum Matthew

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/096

  Transfer learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• H04N21/439 Primary

  Processing of audio elementary streams · CPC title