Open bmond opened 6 years ago
Hi bmond, The problem is the mismatched data distribution. The model has been trained on the RECOLA database (https://diuf.unifr.ch/diva/recola/). For more detail, see the paper: (https://www.isca-speech.org/archive/Interspeech_2016/pdfs/1124.PDF). You may retrain a new classifier for your problem. See the guidance here: (https://github.com/openXBOW/openXBOW). I hope it helps.
I have tested with different kind of files where emotions are angry happy sad or calm. I am supposed to get valence and arousal values in such a way that 1st quadrant means excited/happy || 2nd quadrant means angry || 3rd quadrant means sad || 4th quadrant means calm. However for every expression I am getting same quadrant only and that is 1st. Am I doing something wrong? Do I need to train first with some data for better result or do I need to do noise filtering beforehand?
The result I am getting where arousal and valence as: angry1 arousal: 0.158374 valence: 0.530165
Angry2 arousal: 0.155097 valence: 0.163162
Calm arousal: 0.228325 valence: 0.144437
Happy1 arousal: 0.175005 valence: 0.350349
Happy2 arousal: 0.248143 valence: 0.200598
Sad1 arousal: 0.144986 valence: 0.358285
Sad2 arousal: 0.276521 valence: 0.342779
audioAnalytics.zip