Indirect acquisition of instrumental gesture based on signal, physical and perceptual information
NIME '03 Proceedings of the 2003 conference on New interfaces for musical expression
Inferring control inputs to an acoustic violin from audio spectra
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Transcription and expressiveness detection system for violin music
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Statistical modeling of bowing control applied to violin sound synthesis
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on virtual analog audio Effects and musical instruments
Performance Control Driven Violin Timbre Model Based on Neural Networks
IEEE Transactions on Audio, Speech, and Language Processing
Proceedings of the 20th ACM international conference on Multimedia
Physical modelling and supervised training of a virtual string quartet
Proceedings of the 21st ACM international conference on Multimedia
Hi-index | 0.00 |
Acquisition of instrumental gestures in musical performances is an important task used in different fields ranging from acoustics and sound synthesis to motor learning or electroacoustic performances. The most common approach for acquiring gestures is by means of a sensing system. The direct measurement involves the use of usually expensive sensors with some degree of intrusivity and generally entails complex setups. Indirect acquisition is based on the processing of the audio signal and it is usually informed on acoustical or physical properties of the sound or sound production mechanism. In this paper we present an indirect acquisition method of violin controls from an audio signal based on learning of empirical data that is previously collected with a highly accurate sensing system. The learning consists of training of statistical models with a database of multimodal data from violin performances. The database includes audio spectral features and instrumental controls (bow tilt, bow force, bow velocity, bowing distance to the bridge and played string) and is designed to sample most part of the violin performance control space. We expect that once the indirect acquisition system is trained, no sensors should be required, so the indirect acquisition becomes a low-cost and non-intrusive acquisition method.