1. A method of classifying a mood of a music file, comprising: extracting a Modified Discrete Cosine Transformation-based timbre feature from a compressed domain of a music file; extracting a Modified Discrete Cosine Transformation-based tempo feature from the compressed domain of the music file; and classifying a mood of the music file based on the extracted timbre feature and the extracted tempo feature, wherein the extracting the Modified Discrete Cosine Transformation-based timbre feature from the compressed domain of the music file comprises: extracting Modified Discrete Cosine Transformation coefficients by decoding a part of the music file; selecting the Modified Discrete Cosine Transformation coefficients of a predetermined number of sub-bands from the extracted Modified Discrete Cosine Transformation coefficients; and extracting a spectral centroid, a bandwidth, a rolloff, and a flux from the selected Modified Discrete Cosine Transformation coefficients, wherein the classifying the mood of the music file comprises: classifying a genre of the music file based on the extracted timbre feature; and reclassifying a category of the music file of the genre when uncertainty of a genre classification result is greater than a predetermined value; and wherein, in the reclassifying a category of the music file of the genre, the category of the music file of the genre is reclassified based on the extracted tempo feature.