The idea is to train my Nano 33 BLE sense so that it will recognize the name of a color.
I took 5000ms samples where 1x the color name is mentioned, in total 4 colors names + 1 background noise (Nano 33 BLE microphone does not give a high output ;-(
Total +/- 5min. data.
The Time series data Window size is set to 1000ms and Windows increase to 500ms
I’ve added Audio (MFCC) processing block and the Neural Network (Keras) learning block
In Feature explorer I see a dense cloud … doesn’t bode well.
Training Performance is bad at 47% with 1000 training cycles
- What can I improve with parameters?
- Did I choose the right Processing and Learning Block?
- This case is possible or is ML not suitable for this?