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. A computing device comprising:
one or more processors; and
a non-transitory, computer-readable medium storing instructions that, when executed by the one or more processors, cause the computing device to perform a set of operations comprising:
determining that a neural network model does not satisfy a training threshold;
training the neural network model by updating at least one weight corresponding to at least one entry in training data, wherein the training data includes a plurality of reference audio signals each associated with a variant of music; and
transmitting the trained neural network model to a media unit.
2. The computing device of claim 1 , wherein determining that the neural network model does not satisfy a training threshold comprises determining that a predetermined accuracy threshold of the training data has not been satisfied.
3. The computing device of claim 1 , wherein the variant of music is a music genre.
4. The computing device of claim 1 , wherein the variant of music is a music volume.
5. The computing device of claim 1 , wherein the variant of music is one or more of: (i) a music mood; (ii) a music tempo; and (iii) a music tempo.
6. The computing device of claim 1 , wherein the media unit is media unit in an automobile.
7. The computing device of claim 1 , wherein transmitting the trained neural network model to a media unit comprises transmitting the trained neural network model to a media unit when the neural network model satisfies the training threshold.
8. The computing device of claim 1 , wherein the set of operations further comprises:
prior to determining that a neural network model does not satisfy a training threshold, obtaining the training data; and
organizing the training data into a plurality of entries based on the plurality of reference audio signals.
9. A non-transitory computer readable storage medium comprising instructions which, when executed, cause at least one processor to at least:
determine that a neural network model does not satisfy a training threshold;
train the neural network model by updating at least one weight corresponding to at least one entry in training data, wherein the training data includes a plurality of reference audio signals each associated with a variant of music; and
transmit the trained neural network model to a media unit.
10. The non-transitory computer readable storage medium of claim 9 , wherein the variant of music is a music genre.
11. The non-transitory computer readable storage medium of claim 9 , wherein the variant of music is a music volume.
12. The non-transitory computer readable storage medium of claim 9 , wherein the variant of music is one or more of: (i) a music mood; (ii) a music tempo; and (iii) a music tempo.
13. The non-transitory computer readable storage medium of claim 9 , wherein the media unit is media unit in an automobile.
14. The non-transitory computer readable storage medium of claim 9 , further comprising instructions which, when executed, cause at least one processor to at least:
prior to determining that a neural network model does not satisfy a training threshold, obtain the training data; and
organize the training data into a plurality of entries based on the plurality of reference audio signals.
15. A method comprising:
determining that a neural network model does not satisfy a training threshold;
training the neural network model by updating at least one weight corresponding to at least one entry in training data, wherein the training data includes a plurality of reference audio signals each associated with a variant of music; and
transmitting the trained neural network model to a media unit.
16. The method of claim 15 , wherein the variant of music is a music genre.
17. The method of claim 15 , wherein the variant of music is a music volume.
18. The method of claim 15 , wherein the variant of music is one or more of: (i) a music mood; (ii) a music tempo; and (iii) a music tempo.
19. The method of claim 15 , wherein the media unit is media unit in an automobile.
20. The method of claim 15 , further comprising:
prior to determining that a neural network model does not satisfy a training threshold, obtaining the training data; and
organizing the training data into a plurality of entries based on the plurality of reference audio signals.