Most musicians strive to create performances that are both technically correct and musically expressive. They make specific changes to their performance to bring out the music's expressive potential. Researchers have attempted to identify these particular changes in order to better understand how expressive performances are created. This is an important undertaking because understanding how expressive performances are created, communicated, learned, and experienced, gives musicians and music teachers knowledge they can use to enhance their performing and teaching with regard to artistry. This study investigates a teaching strategy for enhancing aspects of individual vocal expressive performance (e.g. dynamics, timing, and so on).
There is an increasing amount of empirical research targeting specific components of the complicated and "ill-structured domain" (Spiro, Feltovich, Jacobson, & Coulson, 1995) that is expressive performance (EP). Comprehensive reviews by De Poli (2004), Gabrielsson (1999, 2003), Goebl, Dixon, De Poli, Friberg, Bresin, & Widmer (2008), Juslin and Laukka (2004), Juslin and Sloboda (2001), and Widmer and Goebl (2004) highlight approaches researchers have taken in investigating EP. These approaches include the communication of emotion in music performance, statistical analysis of expressive performances, computational modeling of EP, strategies for teaching EP, and factors that inhibit individual EP.
Communication of emotion in EP
Regarding the communication of emotion in musical performance, Juslin (1999) found that listener's perceptions of emotional expression in music change when cues (e.g. tempo, dynamics, timing, and articulation) are altered. In a psychological approach to investigating communication of emotion in music, Gabrielsson (2002) found that emotional perception (perceiving emotional expression in music but not necessarily being influenced by that emotion) and emotional induction (having an emotional response to hearing emotions in music) are affected by musical, personal, and situational factors. Other psychological research focuses on understanding the connection between hearing music, and emotional perceptions or responses (Gabrielsson & Juslin, 1996; Gabrielsson & Lindström, 2001; Juslin & Sloboda, 2001).
Statistical analysis of EP
Research in statistical analysis of expressive performance attempts to identify and explore discrete components of EP such as expressive timings and dynamics, or to identify the expressive ways that expert performances vary from one to the next. Data usually come from audio recordings of expert performances (Cook, 2007; Repp, 1992) or from computer-controlled acoustic pianos or other measurement devices (Bresin & Battel, 2000; Repp, 1995). This line of research is helpful in understanding the exact changes musicians make to their performance, and provides performers with concrete suggestions for enhancing their own EP.
Computational modeling of EP
EP has also been studied through a method known as computational modeling. Ramirez, Hazan, Maestre, and Serra (2008) explain that, "In these approaches, humans are responsible for devising a theory or a mathematical model that captures different aspects of musical EP" (p. 38). Also known as kinematic models, computational models typically propose a correlation between the laws of physical motion, and timing in music performance (Friberg, Sundberg, & Frydén, 2000; Honing, 2003; Sundberg, 2000). Widmer and Goebl's (2004) review of computational models places special emphasis on four prominent models: (a) the structure-level model of timing and dynamics (Todd, 1985, 1992), (b) the rule-based performance model (Friberg, 1991; Friberg & Sundberg, 1987; Sundberg, Friberg, & Frydén, 1989), (c) the mathematical model of musical structure and expression (Mazolla, 2002; Mazzola & Göller, 2002), and (d) machine learning models (Widmer, 2002; Widmer & Tobudic, 2003). …