In this paper, we describe a multi-level approach for the extraction of instrumental gesture parameters taken from the characteristics of the signal captured by a microphone and basedonthe knowledge of physical mechanisms taking place on the instrument. We also explore the relationships between some features of timbre and gesture parameters, taking as a starting point for the exploration the timbre descriptors commonlyusedbyprofessionalmusicians when they verbally describe the sounds they produce with their instrument. Finally, we present how this multi-level approach can be applied to the study of the timbre space of the classical guitar.

In this paper, the control of a physical model of a trumpet is studied. Although this model clearly describes the mechanical and acoustical phenomena that are perceptually relevant, additional constraints must be imposed on the control parameters. In contrast with the model where the tube length can be varied continuously, only seven different tube lengths can be obtained with a real instrument. By studying the physical model and its implementation, different relationships between the control parameters and signal characteristics are identified. These relationships are then used to obtain the best set of tube lengths with respect to a given tuning frequency.

Using sensors to extract gestural information for control parameters of digital audio effects is common practice. There has also been research using machine learning techniques to classify specific gestures based on audio feature analysis. In this paper, we will describe our experiments in training a computer to map the appropriate audio-based features to look like sensor data, in order to potentially eliminate the need for sensors. Specifically, we will show our experiments using the ESitar, a digitally enhanced sensor based controller modeled after the traditional North Indian sitar. We utilize multivariate linear regression to map continuous audio features to continuous gestural data. 1.

In this paper we address the problem of automatically determining the control parameters of a physical model in order to simulate a given sound. Typically, the mathematical inversion of these models is very difficult since they are nonlinear systems with delayed feedback. Therefore, techniques from the pattern recognition field are proposed which can be applied to a very large class of systems. After discussing the general methodology, we focus on two subproblems being the large evaluation cost and the physical constraints that must be imposed on the physical model.

In this paper, the control of a physical model of a trumpet is studied. Although this model clearly describes the mechanical and acoustical phenomena that are perceptually relevant, additional constraints must be imposed on the control parameters. In contrast with the model where the tube length can be varied continuously, only seven different tube lengths can be obtained with a real instrument. By studying the physical model and its implementation, different relationships between the control parameters and signal characteristics are identified. These relationships are then used to obtain the best set of tube lengths with respect to a given tuning frequency.

This paper focuses on the extraction of the excitation point location on a guitar string by an iterative estimation of the structural parameters of the spectral envelope. We propose a general method to estimate the plucking point location, working into two stages: starting from a measure related to the autocorrelation of the signal as a first approximation, a weighted least-square estimation is used to refine a FIR comb filter delay value to better fit the measured spectral envelope. This method is based on the fact that, in a simple digital physical model of a plucked-string instrument, the resonant modes translate into an all-pole structure while the initial conditions (a triangular shape for the string and a zero-velocity at all points) result in a FIR comb filter structure.

A new estimation technique is proposed which computes the control parameters of a physical model of a trumpet in order to simulate a recording of a real instrument. First, the physical constraints of the instrument and the prior knowledge about how a player controls a trumpet are described. This is taken into account during the design of the data set and guarantees that these constraints are respected. Then, an estimation procedure minimizes two perceptual similarity criteria in function of the control parameters. The first criterium expresses the difference of the spectral envelopes and the second one the difference in fundamental frequency. An optimization technique is proposed that yields an optimal solution for the fundamental frequency, and a conditional suboptimal solution for the spectral envelope. A robust implementation of the technique was developed for which it is shown that the estimated parameters are unique and that the optimization does not suffer from local minima.

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This article presents a method to visually detect and recognize fingering gestures of the left hand of a guitarist. This method has been developed following preliminary manual and automated analysis of video recordings. These first analyses led to some important findings about the design methodology of a vision system for guitarist fingering, namely the focus on the effective gesture, the consideration of the action of each individual finger, and a recognition system not relying on comparison against a knowledge base of previously learned fingering positions. Motivated by these results, studies on three aspects of a complete fingering system were conducted: the first on finger tracking; the second on strings and frets detection; and the last one on movement segmentation. Finally, these concepts were integrated into a prototype and a system for left hand fingering detection was developed.

www.elsevier.com/locate/apacoust A time-domain approach to estimating the plucking point of guitar tones obtained with an under-saddle pickup