genre classification so farwe have had the luxury that every training data instance could easily be described by vector of feature values in the iris datasetfor examplethe flowers are represented by vectors containing values for the length and width of certain aspects of flower in the text-based exampleswe could transform the text into bag-of-words representation and manually craft our own features that captured certain aspects of the texts it will be different in this howeverwhen we try to classify songs by their genre or how would wefor instancerepresent three-minute long songshould we take the individual bits of its mp representationprobably notsince treating it like text and creating something such as "bag of sound biteswould certainly be way too complex somehowwe will nevertheless have to convert song into number of values that describes it sufficiently sketching our roadmap this will show us how we can come up with decent classifier in domain that is outside our comfort zone for onewe will have to use sound-based featureswhich 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 classeswhereas we have only encountered binary-classification problems up to now in additionwe will get to know new ways of measuring classification performance let us assume scenario where we find bunch of randomly named mp files on our hard diskwhich are assumed to contain music our task is to sort them according to the music genre into different folders such as jazzclassicalcountrypoprockand metal