Methods and apparatus to adjust audio playback settings
Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings. 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; an EQ personalization manager to; generate a personalized EQ setting; and an EQ adjustment implementor to: blend the personalized EQ setting and the filter coefficient to generate a blended equalization; and apply the blended equalization to the audio signal in a first duration.
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 to be played at a playback device;
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;
an EQ personalization manager to generate a personalized EQ setting based on at least an equalization profile associated with an audio application locally stored on a device, the audio application sourcing the audio signal to the playback device; and
an EQ adjustment implementor to:
blend the personalized EQ setting and the filter coefficient to generate a blended equalization; and
apply the blended equalization to the audio signal in a first duration.
2. The apparatus of claim 1 , further including a historical EQ manager to:
generate the personalized EQ setting based on past personalization settings; and
in response to historical equalization being enabled, adjust the personalized EQ setting based on EQ settings associated with a previous period of time.
3. The apparatus of claim 1 , further including a user input analyzer to:
in response to availability of data indicative of preferences of a user, determine EQ parameters based on the data indicative of preferences of the user, the EQ parameters corresponding to audio playback settings; and
adjust the personalized EQ setting based on the EQ parameters determined based on the data indicative of preferences of the user.
4. The apparatus of claim 1 , further including a device parameter analyzer to:
in response to availability of location data of the playback device, adjust the personalized EQ setting based on the location data of the playback device;
in response to availability of identification of a user, adjust the personalized EQ setting based on a profile associated with the user; and
adjust the personalized EQ setting based on the source of the audio signal.
5. The apparatus of claim 1 , wherein the EQ adjustment implementor is to apply weights to a first personalized EQ setting, a second personalized EQ setting, and the filter coefficient to generate the blended equalization.
6. The apparatus of claim 1 , wherein the personalized EQ setting is additionally based on at least one of EQ settings associated with a previous period of time, data indicative of preferences of a user, location data of the playback device, or a profile associated with the user.
7. 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.
8. 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.
9. 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 to be played at a playback device;
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;
generate a personalized EQ setting based on at least an equalization profile associated with an audio application locally stored on a device, the audio application sourcing the audio signal to the playback device;
blend the personalized EQ setting and the filter coefficient to generate a blended equalization; and
apply the blended equalization to the audio signal in a first duration.
10. The non-transitory computer readable storage medium of claim 9 , wherein the instructions, when executed, cause the one or more processors to:
generate the personalized EQ setting based on past personalization settings; and
in response to historical equalization being enabled, adjust the personalized EQ setting based on EQ settings associated with a previous period of time.
11. The non-transitory computer readable storage medium of claim 9 , wherein the instructions, when executed, cause the one or more processors to:
in response to availability of data indicative of preferences of a user, determine EQ parameters based on the data indicative of preferences of the user, the EQ parameters corresponding to audio playback settings; and
adjust the personalized EQ setting based on the EQ parameters determined based on the data indicative of preferences of the user.
12. The non-transitory computer readable storage medium of claim 9 , wherein the instructions, when executed, cause the one or more processors to:
in response to availability of location data of the playback device, adjust the personalized EQ setting based on the location data of the playback device;
in response to availability of identification of a user, adjust the personalized EQ setting based on a profile associated with the user; and
adjust the personalized EQ setting based on the source of the audio signal.
13. The non-transitory computer readable storage medium of claim 9 , wherein the instructions, when executed, cause the one or more processors to apply weights to a first personalized EQ setting, a second personalized EQ setting, and the filter coefficient to generate the blended equalization.
14. The non-transitory computer readable storage medium of claim 9 , wherein the personalized EQ setting is additionally based on at least one of EQ settings associated with a previous period of time, data indicative of preferences of a user, location data of the playback device, or a profile associated with the user.
15. The non-transitory computer readable storage medium of claim 9 , 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.
16. The non-transitory computer readable storage medium of claim 9 , 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. A method comprising:
generating a query to a neural network, the query including a representation of a sample of an audio signal at a playback device;
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;
generating a personalized EQ setting based on at least an equalization profile associated with an audio application locally stored on a device, the audio application sourcing the audio signal to the playback device;
blending the personalized EQ setting and the filter coefficient to generate a blended equalization; and
applying the blended equalization to the audio signal in a first duration.
18. The method of claim 17 , further including:
generating the personalized EQ setting based on past personalization settings; and
in response to historical equalization being enabled, adjusting the personalized EQ setting based on EQ settings associated with a previous period of time.
19. The method of claim 17 , further including:
in response to availability of data indicative of preferences of a user, determining EQ parameters based on the data indicative of preferences of the user, the EQ parameters corresponding to audio playback settings; and
adjusting the personalized EQ setting based on the EQ parameters determined based on the data indicative of preferences of the user.
20. The method of claim 17 , further including:
in response to availability of location data of the playback device, adjusting the personalized EQ setting based on the location data of the playback device;
in response to availability of identification of a user, adjusting the personalized EQ setting based on a profile associated with the user; and
adjusting the personalized EQ setting based on the source of the audio signal.
21. The method of claim 17 , further including applying weights to a first personalized EQ setting, a second personalized EQ setting, and the filter coefficient to generate the blended equalization.
22. The method of claim 17 , wherein the personalized EQ setting is additionally based on at least one of EQ settings associated with a previous period of time, data indicative of preferences of a user, location data of the playback device, or a profile associated with the user.
23. The method of claim 17 , wherein the representation of the sample of the audio signal corresponds to a constant Q transform representation of the sample of the audio signal.