I currently trying to train a neural network capable of  mapping a input (400 input features) to a output vector of 13 elements. The input features is an fixed length audio sample, and the output is a feature vector extracted from it.I made a simple network consisting of one layer:model.add(Dense(output_dim=13, input_dim=400, init=normal,activation=relu))Trained it for 10 epochs gives me these results: The training shows that the it does become better, but how do i improve it ?Updated model:Training and testing: Plotting (predicted_output - actual_output)[0]Plot  showing the different between the predicted_output and the actual_output for only the first feature. overlap plot:
Network design for extracting MFCC features from audio samples
neural network;regression;keras