Music Speech Discrimination
We present a classifier that classifies audio samples into music or speech. Our classifier uses MFCCs and chroma features which capture timbre and pitch information from an audio sample. We demonstrate the separability of music speech samples by using PCA to reduce our feature space down to two dimensions. We implemented SVM, GDA, and Naive Bayes classifiers on our dataset. The Naive Bayes classifier gives 91% accuracy over the dataset with 4 fold cross validation. When tested on completely unseen dataset this same classifier achieves an accuracy of 98.75%