# Can constant and time-dependent features be mixed?

Suppose I'm trying to perform supervised classification on a list of birds into either nocturnal or diurnal. The only features I have regarding each bird are

• its wingspan (float),
• its colour (multi-class: white, brown, red, yellow, blue, green, black),
• its diet (binary: carnivore or not),
• its habitat (multi-class: ocean, river, mountain, desert), and
• a sound recording of their call/song (time series of floats)

Let's say I want to use a neural network. A feed-forward neural network will work just fine for the first four features. (The wingspan will simply be one of the nodes in the input layer whereas colour, diet, and habitat will be decomposed into several input nodes, presumably via one-hot encoding.)

However, how shall one deal with the last feature, namely the recording of the bird sounds? If this were the only feature, the natural choice would be a recurrent neural network (RNN), but then what should one do with the first four constant features? Clearly, copying them at each recursion of the RNN is both redundant and contrary to the "spirit" of RNNs since, for example, the colour of the bird isn't expected to change from one sound bit to the next.

In other words, how does the architecture of the neural net account for such mixed features? (The same question applies to other classifiers such as support vector machines.)

• No, you train once. Your output is the bird class, right? so the gradient flows back to the merge layer, and there it flows into the two input columns. So, for this schema, you'd split your training data in two: the sounds and the discrete features, and instead of calling fit(X_train, y_train) you'd say something like fit([X_train_discrete, X_train_songs], y_train) Nov 2 '17 at 14:42