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 tangible, non-transitory computer-readable medium having stored thereon instructions that, when executed, cause one or more processors to perform a set of operations comprising:
analyzing, 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;
determining an input volume of the audio signal;
in response to the determining the classification group and the input volume, applying a gain value to the audio signal, wherein the gain value modifies the first volume level to a second volume level; and
applying a compression value to the audio signal, wherein the compression value modifies the second volume level to a third volume level that satisfies a target volume threshold.
2 . The tangible, non-transitory computer-readable medium of claim 1 , wherein applying a compression value to the audio signal further comprises: (i) if the gain value increases, the compression value is decreased; and (ii) if the gain value decreases, the compression value is increased.
3 . The tangible, non-transitory computer-readable medium of claim 1 , wherein the set of operations further comprises determining if a source of the input audio signal has changed.
4 . The tangible, non-transitory computer-readable medium of claim 3 , 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, and (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.
5 . The tangible, non-transitory computer-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, and (3) a presence of an instrument in music represented by the input audio signal.
6 . The tangible, non-transitory computer-readable medium of claim 1 , wherein the set of operations further comprises determining a classification gain value based on the classification group and the input volume.
7 . The tangible, non-transitory computer-readable medium of claim 6 , wherein the set of operations further comprises determining a target gain 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.
8 . The tangible, non-transitory computer-readable medium of claim 7 , 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.
9 . A computer-implemented method for volume adjustment, comprising:
analyzing, 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;
determining an input volume of the audio signal;
in response to the determining the classification group and the input volume, applying a gain value to the audio signal, wherein the gain value modifies the first volume level to a second volume level; and
applying a compression value to the audio signal, wherein the compression value modifies the second volume level to a third volume level that satisfies a target volume threshold.
10 . The computer-implemented method of claim 9 , wherein applying a compression value to the audio signal further comprises: (i) if the gain value increases, the compression value is decreased; and (ii) if the gain value decreases, the compression value is increased.
11 . The computer-implemented method of claim 9 , further comprising determining if a source of the input audio signal has changed.
12 . The computer-implemented method of claim 11 , 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, and (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.
13 . The computer-implemented 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, and (3) a presence of an instrument in music represented by the input audio signal.
14 . The computer-implemented method of claim 9 , further comprising determining a classification gain value based on the classification group and the input volume.
15 . The computer-implemented method of claim 14 , further comprising determining a target gain 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.
16 . The computer-implemented method of claim 15 , 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.
17 . A computing device comprising:
one or more processors; and
tangible, non-transitory computer-readable medium having stored thereon instructions that, when executed, cause one or more processors to perform a set of operations comprising:
analyzing, 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;
determining an input volume of the audio signal;
in response to the determining the classification group and the input volume, applying a gain value to the audio signal, wherein the gain value modifies the first volume level to a second volume level; and
applying a compression value to the audio signal, wherein the compression value modifies the second volume level to a third volume level that satisfies a target volume threshold.
18 . The computing device of claim 17 , wherein applying a compression value to the audio signal further comprises: (i) if the gain value increases, the compression value is decreased; and (ii) if the gain value decreases, the compression value is increased.
19 . The computing device of claim 1 , wherein the set of operations further comprises determining if a source of the input audio signal has changed.
20 . The computing device of claim 19 , 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, and (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.