Hi
I understand that the problem is with the excessive number of "False Positives" in the classification of footsteps from wood environment sound. False positive indicates that the Machine Learning model somehow classifies the non-footsteps as footsteps. The following can be the reasons for the same
- Imbalanced dataset: The number of non-footstep data points is proportionately larger than footstep data points. "data-imbalance."
- Non-characteristic Features: The features used for classification may not capture the distinctive characteristics appropriate for the classification problem at hand.
- Model Overfitting: Sometimes, the model may be complex enough to fit to noise(outliers) and can classify incorrectly.
Above are a few reasons that can lead to excessive False Positives. I would suggest you look at the mis classified data closely, extract the right features using the "AI for Audio" from the "Audio Toolbox (mathworks.com)" that is distinctive of the classification problem and try to observe if there is a pattern to the miss-classification.
I suggest you refer to the documentation and examples in "Audio Toolbox (mathworks.com)" for a better resolution to your problem.
I hope this helps!
Cheers,
Nayan
