I have histograms of audio signals where they have bimodal normal distribution. What I want to do is to detect these subpopulations inorder to have a threshhold, this is meant as a preprocessing step so it can be used to make decisions based on it (the black line in the examples below).

I am thinking of implementing a K-Means clustering algorithm to detect distributions. Now my question is:

1. Is this the correct solution? Choosing bad initial means is worrying me that the algorithm will fail to cluster correctly.

2. What are other solutions to separate the two distributions, I have looked at GMM, but am not sure how it helps.

3. if K-Means is somehow appropriate for solving such a problem how should I select the initial means, or does it depend mostly on the data ?

_{Note that I am new to this field so I hope to correct me if I made any horrible mistakes}

I have histograms of audio signals where they have bimodal "normal" distribution. What I want to do is to detect these subpopulations inorder to have a threshold, this is meant to divide the values into background noise and speech, the background noise and speech as each is meant to have its normal distribution. This is a preprocessing step so it can be used to make later decisions based on it.

Here is my time series of energy value (in DB) and below its corresponding histogram

I am thinking of implementing a K-Means clustering algorithm to detect distributions. Now my question is:

1. Is this the correct solution? Choosing bad initial means is worrying me that the algorithm will fail to cluster correctly.

2. What are other solutions to separate the two distributions, I have looked at GMM, but am not sure how it helps.

3. if K-Means is somehow appropriate for solving such a problem how should I select the initial means, or does it depend mostly on the data ?

_{Note that I am new to this field so I hope to correct me if I made any horrible mistakes}