Genre classification using harmony rules induced from automatic chord transcriptions

We present an automatic genre classification technique mak- ing use of frequent chord sequences that can be applied on symbolic as well as audio data. We adopt a first-order logic representation of harmony and musical genres: pieces of music are represented as lists of chords and musical gen- res are seen as context-free definite clause grammars using subsequences of these chord lists. To induce the context- free definite clause grammars characterising the genres we use a first-order logic decision tree induction algorithm. We report on the adaptation of this classification frame- work to audio data using an automatic chord transcription algorithm. We also introduce a high-level harmony rep- resentation scheme which describes the chords in term of both their degrees and chord categories. When compared to another high-level harmony representation scheme used in a previous study, it obtains better classification accura- cies and shorter run times. We test this framework on 856 audio files synthesized from Band in a Box files and cov- ering 3 main genres, and 9 subgenres. We perform 3-way and 2-way classification tasks on these audio files and ob- tain good classification results: between 67% and 79% ac- curacy for the 2-way classification tasks and between 58% and 72% accuracy for the 3-way classification tasks.