This paper analyzes and compares different methods for audio chroma feature extraction. The chroma feature is a descriptor, which represents the tonal content of a musical audio signal in a condensed form. Therefore chroma features can be considered as important prerequisite for high-level semantic analysis, like chord recognition or harmonic similarity estimation. A better quality of the extracted chroma feature enables much better results in these high-level tasks. In order to discover the quality of chroma features, seven different state-of-the-art chroma feature extraction methods have been implemented. Based on an audio database, containing 55 variations of triads, the output of these algorithms is critically evaluated. The best results were obtained with the Enhanced Pitch Class Profile.
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