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Mion, Luca - De Poli, Giovanni (2007) Comparing affective and sensorial expressions in music performance by machine learning techniques. In: International Workshop on the Biology and Genetics of Music, 20-22 May 2007, Bologna, Italy.

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Abstract (inglese)

We present a paradigm for expression understanding in music performance based on a joint semantic space, described by both affective and sensorial adjectives. Machine learning techniques were used to select the most relevant audio features allowing to recognize different expressive intentions both in the affective (Valence-Arousal) and sensory (Kinematics-Energy) spaces. By Sequential Forward Selection and Principal Component Analysis we derived a set of features for a general description. Good expression recognition was achieved using these cues. The positions of emotional and sensory adjectives in the feature spaces are in good agreement with their positions in the corresponding affective and sensorial spaces.
Then, all the performances described by the selected features were projected on a joint 2D space. Thus we exploited the significance of the selected features in order to see how all the adjectives are jointly organized in the feature space, and to explain the found relations relating the space to a semantic interpretation and possible association among sensorial and affective labels. Three main cluster emerged: (a) Hard/Heavy/Angry, (b) Sad/Calm/Soft/, (c) Light/Happy. An interpretation of the clusters based on action and physical analogy can be devised. When using the physical analogy, force is often subjectively considered as the cause and movement (velocity or position) as the effect. The cause-effect relation is represented by the admittance Y which mathematically describes the dynamic mapping and the qualitative behaviour from force to velocity by an integral-differential equation. We can distinguish three kinds of basic behaviours: friction, which determines the amount of effect and thus scales the energy; elasticity, which opposes changes in force; inertia, which opposes changes in movement. This allows us the associate an interpretation to clusters (a) as related to the concept of energy and (low) friction, cluster (b) to elasticity, and cluster (c) to inertia.


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Tipo di EPrint:Contributo a convegno (Poster)
Anno di Pubblicazione:20 Maggio 2007
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:93
Depositato il:06 Giu 2007
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[2] S. Canazza, G. De Poli, A. Roda, and A. Vidolin, “An abstract control space for communication of sensory expressive intentions in music performance”, Journal of the New Music Research, vol. 32, no. 3, pp. 281-294, 2003. Cerca con Google

[3] G. De Poli, F. Avanzini, A. Roda, L. Mion, G. DInca, C. Trestino, D. Pirro, A. Luciani, and N. Castagne, “Towards a multi-layer architecture for multi-modal rendering of expressive actions”, In Proc. 2nd Int. Conf. on Enactive Interfaces, 2005. Cerca con Google

[4] E. Bigand, S. Vieillard, F. Madurell, J. Marozeau and A. Dacquet, “Multidimensional scaling of emotional responses to music: The effect of musical expertise and of the duration of the excerpts”, Cognition and Emotion, vol. 19, no. 8, pp. 1113-1139, 2005. Cerca con Google

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