Methods and apparatus for dynamic volume adjustment via audio classification
Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification. Example apparatus include at least one memory; instructions; and at least one processor to execute the instructions to: analyze, with a neural network, a parameter of an audio signal associated with a first volume level to determine a classification group associated with the audio signal; determine an input volume of the audio signal; determine a classification gain value based on the classification group; determine an intermediate gain value as an intermediate between the input volume and the classification gain value by applying a first weight to the input volume and a second weight to the classification gain value; apply the intermediate gain value to the audio signal, the intermediate gain value to modify the first volume level to a second volume level; and apply a compression value to the audio signal, the compression value to modify the second volume level to a third volume level that satisfies a target volume threshold.
1. A non-transitory machine-readable medium having stored thereon instructions that, when executed, cause one or more processors to perform a set of operations comprising:
analyzing, by a neural network, a parameter of an input audio signal associated with a first input volume level to determine a classification group associated with the input audio signal;
determining an input volume of the input audio signal;
determining a classification gain value based on the classification group;
determining a target gain value as an intermediary value between the input volume and the classification gain value, wherein the target gain value is determined by applying one or more weights to the input volume and the classification gain value; and
generating a gain-adjusted volume by applying the target gain value to the input audio signal.
2. The non-transitory machine-readable medium of claim 1 , wherein the target gain value is determined by applying a first weight to the input volume and a second weight to the classification gain value.
3. The non-transitory machine-readable medium of claim 1 , wherein the set of operations further comprises applying the intermediary value to the input audio signal, wherein the intermediary value modifies the first input volume level to a second volume level.
4. The non-transitory machine-readable medium of claim 3 , wherein the set of operations further comprises applying a compression value to the input audio signal, wherein the compression value modifies the second volume level to a third volume level that satisfies a target volume threshold.
5. The non-transitory machine-readable medium of claim 1 , wherein the set of operations further comprises determining if a source of the input audio signal has changed.
6. The non-transitory machine-readable medium of claim 5 , wherein determining if the source of the input audio signal has changed is based on at least one of: (1) a comparison of a current compressor gain associated with the input audio signal to a previous compressor gain associated with the input audio signal, (2) a comparison of a RMS power associated with the input audio signal to a previous RMS power associated with the input audio signal, or (3) a comparison of a current audio sample value associated with the input audio signal to a previous audio sample value associated with the input audio signal.
7. The non-transitory machine-readable medium of claim 5 , wherein the set of operations further comprises, in response to determining the source of the input audio signal has changed, resetting the intermediary value of the input audio signal.
8. The non-transitory machine-readable medium of claim 1 , wherein the classification group comprises at least one of: (1) a genre of music represented by the input audio signal, (2) a time period of music represented by the input audio signal, or (3) a presence of an instrument in music represented by the input audio signal.
9. A method, comprising:
analyzing, by a neural network, a parameter of an input audio signal associated with a first input volume level to determine a classification group associated with the input audio signal;
determining an input volume of the input audio signal;
determining a classification gain value based on the classification group;
determining a target gain value as an intermediary value between the input volume and the classification gain value, wherein the target gain value is determined by applying one or more weights to the input volume and the classification gain value; and
generating a gain-adjusted volume by applying the target gain value to the input audio signal.
10. The method of claim 9 , wherein the target gain value is determined by applying a first weight to the input volume and a second weight to the classification gain value.
11. The method of claim 9 , wherein the method further comprises applying the intermediary value to the input audio signal, wherein the intermediary value modifies the first input volume level to a second volume level.
12. The method of claim 11 , wherein the method further comprises applying a compression value to the input audio signal, wherein the compression value modifies the second volume level to a third volume level that satisfies a target volume threshold.
13. The method of claim 9 , wherein the method further comprises determining if a source of the input audio signal has changed.
14. The method of claim 13 , wherein determining if the source of the input audio signal has changed is based on at least one of: (1) a comparison of a current compressor gain associated with the input audio signal to a previous compressor gain associated with the input audio signal, (2) a comparison of a RMS power associated with the input audio signal to a previous RMS power associated with the input audio signal, or (3) a comparison of a current audio sample value associated with the input audio signal to a previous audio sample value associated with the input audio signal.
15. The method of claim 13 , wherein the method further comprises, in response to determining the source of the input audio signal has changed, resetting the intermediary value of the input audio signal.
16. The method of claim 9 , wherein the classification group comprises at least one of: (1) a genre of music represented by the input audio signal, (2) a time period of music represented by the input audio signal, or (3) a presence of an instrument in music represented by the input audio signal.
17. An apparatus, comprising:
one or more processors; and
a non-transitory machine-readable medium having stored thereon instructions that, when executed, cause one or more processors to perform a set of operations comprising:
analyzing, by a neural network, a parameter of an input audio signal associated with a first input volume level to determine a classification group associated with the input audio signal;
determining an input volume of the input audio signal;
determining a classification gain value based on the classification group;
determining a target gain value as an intermediary value between the input volume and the classification gain value, wherein the target gain value is determined by applying one or more weights to the input volume and the classification gain value; and
generating a gain-adjusted volume by applying the target gain value to the input audio signal.
18. The apparatus of claim 17 , wherein the target gain value is determined by applying a first weight to the input volume and a second weight to the classification gain value.
19. The apparatus of claim 17 , wherein the set of operations further comprises applying the intermediary value to the input audio signal, wherein the intermediary value modifies the first input volume level to a second volume level.
20. The apparatus of claim 19 , wherein the set of operations further comprises applying a compression value to the input audio signal, wherein the compression value modifies the second volume level to a third volume level that satisfies a target volume threshold.