So far, we have had the luxury that every training data instance could easily be described by a vector of feature values. In the Iris dataset, for example, the flowers are represented by vectors containing values for the length and width of certain aspects of a flower. In the text-based examples, we could transform the text into a bag-of-words representation and manually craft our own features that captured certain aspects of the texts.
It will be different in this chapter, however, when we try to classify songs by their genre. Or how would we, for instance, represent a three-minute long song? Should we take the individual bits of its MP3 representation? Probably not, since treating it like text and creating something such as a "bag of sound bites" would certainly be way too complex. Somehow, we will nevertheless have to convert a song into a number of values that describes it sufficiently.
This chapter will show us how we can come up with a decent classifier in a domain that is outside our comfort zone. For one, we will have to use sound-based features, which are much more complex than the text-based ones that we have used before. And then we will have to learn how to deal with multiple classes, whereas we have only encountered binary-classification problems up to now. In addition, we will get to know new ways of measuring classification performance.
Let us assume a scenario where we find a bunch of randomly named MP3 files on our hard disk, which are assumed to contain music. Our task is to sort them according to the music genre into different folders such as jazz, classical, country, pop, rock, and metal.