Abstract
We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.
Highlights
The advances experienced in the last decades in areas as information, communication, and media technologies have made available a large amount of all kinds of data
Such a database contains all 29 genres present in the lower hierarchical level of the taxonomy presented in Figure 1, and was especially designed to test the strategy face to difficult conditions
The technique uses four features that are integrated over 1-second analysis segments, generating 12summary feature vectors for each segment
Summary
The advances experienced in the last decades in areas as information, communication, and media technologies have made available a large amount of all kinds of data This is true for music, whose databases have grown exponentially since the advent of the first perceptual coders early in the 90’s. This situation demands for tools able to ease searching, retrieving, and handling such a huge amount of data. Among those tools, automatic musical genre classifiers (AMGC) can have a important role, since they could be able to automatically index and retrieve audio data in a human-independent way. AGC could be used to automatically select radio stations playing a particular genre of music
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