  Speaker Diarization is the problem of separating speakers in an audio. There
could be any number of speakers and final result should state when speaker
starts and ends. In this project, we analyze given audio file with 2 channels
and 2 speakers (on separate channel). We train Neural Network for learning when
a person is speaking. We use different type of Neural Networks specifically,
Single Layer Perceptron (SLP), Multi Layer Perceptron (MLP), Recurrent Neural
Network (RNN) and Convolution Neural Network (CNN) we achieve $\sim$92\% of
accuracy with RNN. The code for this project is available at
https://github.com/vishalshar/SpeakerDiarization_RNN_CNN_LSTM
