I am trying to build a real time simple audio recognition that can recognize few keywords. Following this tensor flow tutorial i was able to train a simple neural network. Currently the signal flow looks like this
microphone --> Pulse Code Modulation(PCM - range [-1, 1]) --> stfts --> spectrograms--> mel_spectrograms --> log_mel_spectrograms --> mfcc's --> Neural Network.
So far, I just grab the data from the microphone input and preform some steps and input to Neural network. Not performing any kind of back ground noise reduction or some kind of signal filtering techniques. So far the result are very much influenced by the back-ground noises( single channel sampling frequency 16000 Hz).
I am new to this subject and wondering if you can share some valuable insight and techniques/python packages to enrich the speaker voice before feeding it to neural network.