audio-classifier-keras-cnn | Audio Classifier in Keras using Convolutional Neural Network | Machine Learning library

Sounds can be seen as a 1D image and be worked with with 1D convolutions. Often, dilated convolutions may do a good work, see Wave Nets

Sounds can also be seen as sequences and be worked with RNN layers (but maybe they're too bulky in amount of data for that)

For your case, you need only one output with a 'sigmoid' activation at the end and a 'binary_crossentropy' loss.

• Result = 0 -> no voice
• Result = 1 -> there's voice

When to use 'softmax'?

The softmax function is good for multiclass problems (not your case) where you want only one class as a result. All the results of a softmax function will sum 1. It's intended to be like a probability of each class.

It's mainly used at the final layer, because you only get classes as the final result.

It's good for cases when only one class is correct. And in this case, it goes well with the loss categorical_crossentropy.

Relu and other activations in the middle of the model

These are not very ruled. There are lots of possibilities. I often see relu in image convolutional models.

Important things to know are they "ranges". What are the limits of their outputs?

• Sigmoid: from 0 to 1 -- at the end of the model this will be the best option for your presence/abscence classification. Also good for models that want many possible classes together.
• Tanh: from -1 to 1
• Relu: from 0 to limitless (it simply cuts negative values)
• Softmax: from 0 to 1, but making sure the sum of all values is 1. Good at the end of models that want only 1 class among many classes.