The objective of this research is to model the relationship between actions performed by a violinist and the sound which these actions produce. Violinist actions and audio are captured during real performances by means of a newly developed sensing system from which bowing and audio descriptors are computed. A database is built with this data and used to train a generative model based on neural networks. The model is driven by a continuous sequence of bowing and fingering controls and is able to generate their corresponding sequence of spectral envelopes. The model is used for synthesis, either alone as a purely spectral model, by filling the predicted envelopes with harmonic and noisy components, or coupled with a concatenative synthesizer, where the predicted envelopes are used as time-varying filters to transform the concatenated samples. The combination of sample concatenation with the timbre model allows for the preservation of sound quality inherent in samples, while providing a high level of control. Additionally, we perform an analysis of the violinist control space and the influence of the controls on the timbre.

Excitation-continuous music instrument control patterns are often not explicitly represented in current sound synthesis techniques when applied to automatic performance. Both physical model-based and sample-based synthesis paradigms would benefit from a flexible and accurate instrument control model, enabling the improvement of naturalness and realism. We present a framework for modeling bowing control parameters in violin performance. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing control parameter signals. We model the temporal contour of bow velocity, bow pressing force, and bow-bridge distance as sequences of short Be¿zier cubic curve segments. Considering different articulations, dynamics, and performance contexts, a number of note classes are defined. Contours of bowing parameters in a performance database are analyzed at note-level by following a predefined grammar that dictates characteristics of curve segment sequences for each of the classes in consideration. As a result, contour analysis of bowing parameters of each note yields an optimal representation vector that is sufficient for reconstructing original contours with significant fidelity. From the resulting representation vectors, we construct a statistical model based on Gaussian mixtures suitable for both the analysis and synthesis of bowing parameter contours. By using the estimated models, synthetic contours can be generated through a bow planning algorithm able to reproduce possible constraints caused by the finite length of the bow. Rendered contours are successfully used in two preliminary synthesis frameworks: digital waveguide-based bowed string physical modeling and sample-based spectral-domain synthesis.

Audio fingerprinting technologies allow the identification of audio content without the need of external meta-data or watermark embedding. These audio fingerprinting technologies work by extracting a content-based compact digest that summarizes a recording and comparing them with a previously extracted fingerprint database. In this paper we present a fingerprint scheme that is based on hidden Markov models. This approach achieves a high compaction of the audio signal by exploiting structural redundancies on music and robustness to distortions thanks to the stochastic modeling. In this paper we present the basic functionality of the system as well as some results