Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Musical genre classification is put into context byexplaining about the structures in music and how it is analyzedand perceived by humans. The increase of the music databaseson the personal collection and the Internet has brought a greatdemand for music information retrieval, and especiallyautomatic musical genre classification. In this research wefocused on combining information from the audio signal thandifferent sources. This paper presents a comprehensivemachine learning approach to the problem of automaticmusical genre classification using the audio signal. Theproposed approach uses two feature vectors, Support vectormachine classifier with polynomial kernel function andmachine learning algorithms. More specifically, two featuresets for representing frequency domain, temporal domain,cepstral domain and modulation frequency domain audiofeatures are proposed. Using our proposed features SVM act asstrong base learner in AdaBoost, so its performance of theSVM classifier cannot improve using boosting method. Thefinal genre classification is obtained from the set of individualresults according to a weighting combination late fusionmethod and it outperformed the trained fusion method. Musicgenre classification accuracy of 78% and 81% is reported onthe GTZAN dataset over the ten musical genres and theISMIR2004 genre dataset over the six musical genres,respectively. We observed higher classification accuracies withthe ensembles, than with the individual classifiers andimprovements of the performances on the GTZAN andISMIR2004 genre datasets are three percent on average. Thisensemble approach show that it is possible to improve theclassification accuracy by using different types of domainbased audio features

A Highly Robust Audio Monitoring System for Radio Broadcasting

Proposing a novel approach for monitoringsongs for the radio broadcasting channels is veryimportant for the interest of singers, writers andmusicians in the musical industry. Singers, writers andmusicians have a claim to intellectual property rightsfor their songs broadcast over all the radio channels.According to this intellectual property rights actsingers, writers and musicians should be paid for theirsongs broadcast over all the radio channels. Therefore wepropose a real time audio monitoring approach to solvethis problem which includes our own audio recognitionalgorithm. It is easy to recognize a song, when you providethe original high quality blueprint of the song as input. Butwe can’t expect such kind of audio input from radiochannels since lots of transformations are possible beforereaching the end user or listener. For example, addingenvironmental effects such as noise, adding commercialson the song as watermarks, playing more than one songas a chain without adding any silence between them,playing a part of the song, playing same song in variousspeeds and so on. These transformations cause change inthe uniqueness of particular song and make the problemeven more difficult. The algorithm we proposing is resistantto noise and distortion as well as it is capable of recognizingshort segment of song when broadcasting over the radiochannels. At the end of the processing our system generatesa descriptive report including title of the song, singer of thesong, writer of the song, composer of the song, number oftimes it was played and when it was played for all songs fora particular period for all radio broadcasting channels. Weevaluate our system against various types of real timescenarios and achieved overall higher level of accuracy(96%) at the end