Expressive Concatenative Synthesis by Reusing Samples from Real Performance Recordings

The manipulation of sound properties such as timing, amplitude, timbre, and pitch by different performers and styles is an important fact not to be missed when approaching instrumental sound synthesis. Expressive music performance studies the manipulation of such sound properties in an attempt to understand expression, so that it can be applied to sound synthesis for obtaining expressive instrumental sound in the shape of a synthetic performance. During the past few years, the availability of technology for high-fidelity sound synthesis based on samples has pushed the consolidation of sample-based concatenative synthesizers as the most popular and flexible mean of reconstructing the sound of traditional musical instruments (Schwarz 2006). Recent implementations (Bonada and Serra 2007; Lindemann 2007) have yielded high-quality sound synthesis and often offer a wide range of synthesis parameters, including some related to expression, normally concerning either a note or the transition between two successive notes. However, these parameters must in most of cases be tuned manually, which is extremely time consuming and requires considerable effort and knowledge from the user. Ideally, expressionrelated parameters should be tuned automatically by the synthesis system by applying some prior knowledge about the expressive transformations a particular musician introduces when performing a piece. In the past, such knowledge has been traditionally obtained by empirically studying real expressive performance recordings (e.g., Repp 1992; Todd 1992; Friberg et al. 1998), and more recently, by applying machine-learning techniques (e.g., Widmer 2001; Lopez de Mantaras and Arcos 2002; Ramirez, Hazan, and Maestre 2006a, 2006b). Machine-learning approaches to expressive-performance modeling reside