Methods and apparatus for audio equalization based on variant selection
Methods, apparatus, systems and articles of manufacture are disclosed methods and apparatus for audio equalization based on variant selection. An example apparatus includes a processor to obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music and organize the training data into a plurality of entries based on the plurality of reference audio signals, a training model executor to execute a neural network model using the training data, and a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.
1. An apparatus comprising:
a processor to:
obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music; and
organize the training data into a plurality of entries based on the plurality of reference audio signals;
a training model executor to execute a neural network model using the training data; and
a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.
2. The apparatus of claim 1 , further including an output handler to transmit the neural network model to a media unit in an automobile when the neural network model satisfies the training threshold.
3. The apparatus of claim 1 , wherein the training model executor is to execute the neural network model after the model trainer updates the at least one weight corresponding to one of the entries.
4. The apparatus of claim 1 , wherein the variant of music is a music genre.
5. The apparatus of claim 1 , wherein a number of the plurality of entries is equivalent to a number of the plurality of reference audio signals, and wherein the training data further includes a plurality of equalization curves each associated with at least one of the plurality of reference audio signals.
6. The apparatus of claim 1 , wherein the model trainer is to store the neural network model in a database when the training threshold is satisfied, the neural network model accessible by a media unit in an automobile.
7. The apparatus of claim 1 , wherein the training data includes a plurality of tags associated with the plurality of reference audio signals, the plurality of tags indicating one or more audio engineers associated with the plurality of reference audio signals.
8. A non-transitory computer readable storage medium comprising instructions which, when executed, cause at least one processor to at least:
obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music;
organize the training data into a plurality of entries based on the plurality of reference audio signals;
execute a neural network model using the training data; and
train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.
9. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, further cause the at least one processor transmit the neural network model to a media unit in an automobile when the neural network model satisfies the training threshold.
10. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the at least one processor to execute the neural network model after at least one weight corresponding to one of the entries is updated.
11. The non-transitory computer readable storage medium of claim 8 , wherein the variant of music is a music genre.
12. The non-transitory computer readable storage medium of claim 8 , wherein a number of the plurality of entries is equivalent to a number of the plurality of reference audio signals, and wherein the training data further includes a plurality of equalization curves each associated with at least one of the plurality of reference audio signals.
13. The non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, further cause the at least one processor to store the neural network model in a database when the training threshold is satisfied, the neural network model accessible by a media unit in an automobile.
14. The non-transitory computer readable storage medium of claim 8 , wherein the training data includes a plurality of tags associated with the plurality of reference audio signals, the plurality of tags indicating one or more audio engineers associated with the plurality of reference audio signals.
15. A method comprising:
obtaining training data, the training data including a plurality of reference audio signals each associated with a variant of music;
organizing the training data into a plurality of entries based on the plurality of reference audio signals;
executing a neural network model using the training data; and
training the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.
16. The method of claim 15 , further including transmitting the neural network model to a media unit in an automobile when the neural network model satisfies the training threshold.
17. The method of claim 15 , further including executing the neural network model after at least one weight corresponding to one of the entries is updated.
18. The method of claim 15 , wherein the variant of music is a music genre.
19. The method of claim 15 , wherein a number of the plurality of entries is equivalent to a number of the plurality of reference audio signals, and wherein the training data further includes a plurality of equalization curves each associated with at least one of the plurality of reference audio signals.
20. The method of claim 15 , further including storing the neural network model in a database when the training threshold is satisfied, the neural network model accessible by a media unit in an automobile.
21. The method of claim 15 , wherein the training data includes a plurality of tags associated with the plurality of reference audio signals, the plurality of tags indicating one or more audio engineers associated with the plurality of reference audio signals.
22. An apparatus comprising:
means for processing to:
obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music; and
organize the training data into a plurality of entries based on the plurality of reference audio signals;
means for executing a neural network model using the training data; and
means for training the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.
23. The apparatus of claim 22 , further including means for transmitting the neural network model to a media unit in an automobile when the neural network model satisfies the training threshold.
24. The apparatus of claim 22 , wherein the means for executing is to execute the neural network model after at least one weight corresponding to one of the entries is updated.
25. The apparatus of claim 22 , wherein the variant of music is a music genre.
26. The apparatus of claim 22 , wherein a number of the plurality of entries is equivalent to a number of the plurality of reference audio signals, and wherein the training data further includes a plurality of equalization curves each associated with at least one of the plurality of reference audio signals.
27. The apparatus of claim 22 , wherein the means for training is to store the neural network model in a database when the training threshold is satisfied, the neural network model accessible by a media unit in an automobile.
28. The apparatus of claim 22 , wherein the training data includes a plurality of tags associated with the plurality of reference audio signals, the plurality of tags indicating one or more audio engineers associated with the plurality of reference audio signals.