METHODS AND APPARATUS TO ADJUST AUDIO PLAYBACK SETTINGS BASED ON ANALYSIS OF AUDIO CHARACTERISTICS

• US 20200133622A1
• Filed: 10/23/2019
• Published: 04/30/2020
• Est. Priority Date: 10/24/2018
• Status: Active Grant

• Current Assignee: Gracenote, Inc. (RR Donnelley & Sons Company)
• Original Assignee: Gracenote, Inc. (RR Donnelley & Sons Company)
• Inventors: Coover, Robert, Summers, Cameron Aubrey, Hodges, Todd, Renner, Joseph, Cremer, Markus
• Granted Patent: US 11,218,125 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 3/165 Management of the audio stream, e.g. setting of volume, audio stream path
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G10L 25/30 using neural networks
• CPC Class Codes: G10L 25/51 for comparison or discrimination
• CPC Class Codes: H03F 3/181 Low-frequency amplifiers, e.g. audio preamplifiers
• CPC Class Codes: H03G 5/025 Equalizers; Volume or gain control in limited frequency bands
• CPC Class Codes: H03G 5/165 Equalizers; Volume or gain control in limited frequency bands
• CPC Class Codes: H04N 21/439 Processing of audio elementary streams
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4524 involving the geographical location of the client retrieval from the Internet by querying based on geographical locations G06F16/9537; systems specially adapted for using geographical information in broadcast systems H04H60/70; protocols in which the network application is adapted for the location of the user terminal in communication control or processing H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/462 Content or additional data management, e.g. creating a master electronic program guide from data received from the Internet and a Head-end, controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabilities
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4852 for modifying audio parameters, e.g. switching between mono and stereo
• CPC Class Codes: H04N 9/87 Regeneration of colour television signals H04N9/80 takes precedence
• CPC Class Codes: H04R 2430/01 Aspects of volume control, not necessarily automatic, in sound systems
• CPC Class Codes: H04R 2499/11 Transducers incorporated or for use in hand-held devices, e.g. mobile phones, PDA's, camera's
• CPC Class Codes: H04R 2499/13 Acoustic transducers and sound field adaptation in vehicles
• CPC Class Codes: H04R 3/04 : for correcting frequency response
• CPC Class Codes: H04R 5/04 : Circuit arrangements, e.g. for selective connection of amplifier inputs/outputs to loudspeakers, for loudspeaker detection, or for adaptation of settings to personal preferences or hearing impairments combinations of amplifiers H03F3/68; stereophonic systems H04S

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.

Claims

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;

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.

2. The apparatus of claim 1, wherein the representation of the sample of the audio signal corresponds to a frequency representation of the sample of the audio signal.

3. The apparatus of claim 1, wherein the plurality of audio playback settings includes one or more filters, wherein each of the one or more filters 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 implementer 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 implementer 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;

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 frequency 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 includes one or more filters, wherein each of the one or more filters 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;

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 frequency representation of the sample of the audio signal.

17. The method of claim 15, wherein the plurality of audio playback settings includes one or more filters, wherein each of the one or more filters 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.