Abordagem de Machine Learning para descoberta de regras para performance de guitarra jazz

Um estudo muito interessante de padrões de guitarra Jazz.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules’ performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

Algumas das regras encontradas

3.1.2. Duration Rules

• D1: IF note is the final note of a phrase AND the note appears in the third position of an IP (Narmour) structure THEN shorten note
• D2: IF note duration is longer than a dotted half note AND tempo is Medium (90–160 BPM) THEN shorten note
• D3: IF note duration is less than an eighth note AND note is in a very strong metrical position THEN lengthen note.
3.1.3. Onset Deviation Rules

• T1: IF the note duration is short AND piece is up-tempo (≥ 180 BPM) THEN advance note
• T2: IF the duration of the previous note is nominal AND the note’s metrical strength is very strong THEN advance note
• T3: IF the duration of the previous note is short AND piece is up-tempo (≥ 180 BPM) THEN advance note
• T4: IF the tempo is medium (90–160 BPM) AND the note is played within a tonic chord AND the next note’s duration is not short nor long THEN delay note
3.1.4. Energy Deviation Rules

• E1: IF the interval with next note is ascending AND the note pitch not high (lower than B3) THEN play piano
• E2: IF the interval with next note is descending AND the note pitch is very high (higher than C5) THEN play forte
• E3: IF the note is an eight note AND note is the initial note of a phrase THEN play forte.

Conclusões do estudo

Concretely, the obtained accuracies (over the base-line) for the ornamentation, duration, onset, and energy models of 70%(67%), 56%(50%), 63%(54%), and 52%(43%), respectively. Both the features selected and model rules showed musical significance. Similarities and differences among the obtained rules and the ones reported in the literature were discussed. Pattern similarities between classical and jazz music expressive rules were identified, as well as expected dissimilarities expected by the inherent particular musical aspects of each tradition. The induced rules specificity/generality was assessed by applying them to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

 

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Abordagem de Machine Learning para descoberta de regras para performance de guitarra jazz

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