Foundations of statistical natural language processing
Foundations of statistical natural language processing
Automatic chord recognition from audio using a supervised HMM trained with audio-from-symbolic data
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Music retrieval: a tutorial and review
Foundations and Trends in Information Retrieval
Tonal Description of Polyphonic Audio for Music Content Processing
INFORMS Journal on Computing
Music genre classification using MIDI and audio features
EURASIP Journal on Applied Signal Processing
Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing)
TWO GRAMMATICAL INFERENCE APPLICATIONS IN MUSIC PROCESSING
Applied Artificial Intelligence
Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
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In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.