METHODS AND APPARATUS FOR AUDIO EQUALIZATION

• US 20200136580A1
• 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: Renner, Joseph, Coover, Robert, Cremer, Markus, Summers, Cameron Aubrey
• Granted Patent: US 10,998,872 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 for audio equalization. Example apparatus disclosed herein include a volume adjuster to apply equalization adjustments to an audio signal to generate an equalized audio signal, the equalization adjustments output from a neural network in response to an input feature set; a thresholding controller to: detect an irregularity in a frequency representation of the audio signal after application of the equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold; and adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity; an equalization (EQ) curve generator to generate an EQ curve to apply to the audio signal when the irregularity has been reduced; and a frequency to time domain converter to output the equalized audio signal in a time domain based on the EQ curve.

Claims

1. An apparatus comprising:

a volume adjuster to apply a plurality of equalization adjustments to an audio signal to generate an equalized audio signal, the plurality of equalization adjustments output from a neural network in response to an input feature set including an average volume representation of the audio signal;

a thresholding controller to;

detect an irregularity in a frequency representation of the audio signal after application of the plurality of equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold; and

adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity;

an equalization (EQ) curve generator to generate an EQ curve to apply to the audio signal when the irregularity has been reduced; and

a frequency to time domain converter to output the equalized audio signal in a time domain based on the EQ curve.

2. The apparatus of claim 1, further including an energy calculator to:

determine a first root mean square (RMS) value of the frequency representation of the audio signal prior to application of the plurality of equalization adjustments;

determine a second RMS value of the frequency representation of the audio signal after reduction of the irregularity; and

determine a ratio between the second RMS value and the first RMS value.

3. The apparatus of claim 1, further including a volume normalizer to:

determine whether a ratio between (1) a first RMS value of the frequency representation of the audio signal after reduction of the irregularity and (2) a second RMS value of the frequency representation of the audio signal prior to application of the plurality of equalization adjustments exceeds a threshold associated with acceptable change in energy of the audio signal; and

in response to the ratio exceeding the threshold, apply a normalization of a gain of the frequency representation of the audio signal.

4. The apparatus of claim 1, wherein the plurality of equalization adjustments includes a plurality of volume adjustment values corresponding to a plurality of frequency ranges.

5. The apparatus of claim 1, wherein the thresholding controller is to:

select a plurality of frequency values in the frequency representation of the audio signal;

determine a plurality of volume values associated with the plurality of frequency values;

determine a second derivative of the volume over the plurality of frequency values; and

in response to an absolute value of the second derivative exceeding the threshold, adjust the volume at the first frequency value of the adjacent frequency values to reduce the irregularity.

6. The apparatus of claim 1, wherein the plurality of equalization adjustments is based on at least reference audio signals, EQ curves, and tags associated with a plurality of audio engineers that generated the EQ curves, and wherein the neural network determines the plurality of equalization adjustments based on an inference associated with at least the reference audio signals, the EQ curves, and the tags associated with the plurality of audio engineers.

7. The apparatus of claim 6, wherein the input feature set includes the average volume representation of the audio signal and average standard deviation measurements for frequency bins of a frequency representation of the audio signal.

8. A non-transitory compute readable storage medium comprising instructions which, when executed, cause one or more processors to at least:

apply a plurality of equalization adjustments to an audio signal to generate an equalized audio signal, the plurality of equalization adjustments output from a neural network in response to an input feature set including an average volume representation of the audio signal;

detect an irregularity in a frequency representation of the audio signal after application of the plurality of equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold;

adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity;

generate an equalization (EQ) curve to apply to the audio signal when the irregularity has been reduced; and

output the equalized audio signal in a time domain based on the EQ curve.

9. The non-transitory computer readable storage medium of claim 8, wherein the instructions, when executed, cause the one or more processors to:

determine a first root mean square (RMS) value of the frequency representation of the audio signal prior to application of the plurality of equalization adjustments;

determine a second RMS value of the frequency representation of the audio signal after reduction of the irregularity; and

determine a ratio between the second RMS value and the first RMS value.

10. The non-transitory computer readable storage medium of claim 8, wherein the instructions, when executed, cause the one or more processors to:

determine whether a ratio between (1) a first RMS value of the frequency representation of the audio signal after reduction of the irregularity and (2) a second RMS value of the frequency representation of the audio signal prior to application of the plurality of equalization adjustments exceeds a threshold associated with acceptable change in energy of the audio signal; and

in response to the ratio exceeding the threshold, apply a normalization of a gain of the frequency representation of the audio signal.

11. The non-transitory computer readable storage medium of claim 8, wherein the plurality of equalization adjustment includes a plurality of volume adjustment values corresponding to a plurality of frequency ranges.

12. The non-transitory computer readable storage medium of claim 8, wherein the instructions, when executed, cause the one or more processors to:

select a plurality of frequency values in the frequency representation of the audio signal;

determine a plurality of volume values associated with the plurality of frequency values;

determine a second derivative of the volume over the plurality of frequency values; and

in response to an absolute value of the second derivative exceeding the threshold, adjust the volume at the first frequency value of the adjacent frequency values to reduce the irregularity.

13. The non-transitory computer readable storage medium of claim 8, wherein the plurality of equalization adjustments is based on at least reference audio signals, EQ curves, and tags associated with a plurality of audio engineers that generated the EQ curves, and wherein the neural network determines the plurality of equalization adjustments based on an inference associated with at least the reference audio signals, the EQ curves, and the tags associated with the plurality of audio engineers.

14. The non-transitory computer readable storage medium of claim 13, wherein the input feature set includes the average volume representation of the audio signal and average standard deviation measurements for frequency bins of a frequency representation of the audio signal.

15. A method comprising:

applying a plurality of equalization adjustments to an audio signal to generate an equalized audio signal, the plurality of equalization adjustments output from a neural network in response to an input feature set including an average volume representation of the audio signal;

detecting an irregularity in a frequency representation of the audio signal after application of the plurality of equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold;

adjusting a volume at a first frequency value of the adjacent frequency values to reduce the irregularity;

generating an equalization (EQ) curve to apply to the audio signal when the irregularity has been reduced; and

outputting the equalized audio signal in a time domain based on the EQ curve.

16. The method of claim 15, further including:

determining a first root mean square (RMS) value of the frequency representation of the audio signal prior to application of the plurality of equalization adjustments;

determining a second RMS value of the frequency representation of the audio signal after reduction of the irregularity; and

determining a ratio between the second RMS value and the first RMS value.

17. The method of claim 15, further including:

determining whether a ratio between (1) a first RMS value of the frequency representation of the audio signal after reduction of the irregularity and (2) a second RMS value of the frequency representation of the audio signal prior to application of the plurality of equalization adjustments exceeds a threshold associated with acceptable change in energy of the audio signal; and

in response to the ratio exceeding the threshold, applying a normalization of a gain of the frequency representation of the audio signal.

18. The method of claim 15, wherein the plurality of equalization adjustments includes a plurality of volume adjustment values corresponding to a plurality of frequency ranges.

19. The method of claim 15, further including:

selecting a plurality of frequency values in the frequency representation of the audio signal;

determining a plurality of volume values associated with the plurality of frequency values;

determining a second derivative of the volume over the plurality of frequency values; and

in response to an absolute value of the second derivative exceeding the threshold, adjusting the volume at the first frequency value of the adjacent frequency values to reduce the irregularity.

20. The method of claim 15, wherein the plurality of equalization adjustments is based on at least reference audio signals, EQ curves, and tags associated with a plurality of audio engineers that generated the EQ curves, and wherein the neural network determines the plurality of equalization adjustments based on an inference associated with at least the reference audio signals, the EQ curves, and the tags associated with the plurality of audio engineers.