Robust modeling of musical chord sequences using probabilistic N-grams

The modeling of music as a language is a core issue for a wide range of applications such as polyphonic music retrieval, automatic style identification, audio to symbolic music transcription and computer-assisted composition. In this paper, we focus on the modeling of chord sequences by probabilistic N-grams. Previous studies using these models have achieved limited success, due to overfitting and to the use of a single chord labeling scheme. We investigate these issues using model smoothing and selection techniques initially designed for spoken language modeling. This approach is evaluated over a set of songs by The Beatles, considering several chord labeling schemes. Initial results show that the accuracy of N-grams is increased but that additional improvements may still be achieved in the future using more advanced, possibly music-specific, smoothing techniques.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Ricardo Scholz, Emmanuel Vincent, Frédéric Bimbot
• Keywords: Chord Labeling Schemes, ICASSP 2009, Polyphonic Music Retrieval, Signal Processing, Symbolic Music Transcription