tested on new dataset (added ~25m of  falling sounds) classifies much better on falling sounds but a lot of misclassifications for glass breaking. seems to be a data issue more than architecture. current model is very small, plan to either change or increase size. kernel size is currentl 16 and 8 for the 2 2dcnn layers, maybe potential for increase layers/kernel size. Batch size also can be increased to improve training speed. Might offload training and optimization to external server. Potentially look for outside model architectures/pre-trained, might split up work later today.