We propose a novel method for detecting changes in the harmonic content of musical audio signals. Our method uses a new model for Equal Tempered Pitch Class Space. This model maps 12-bin chroma vectors to the interior space of a 6-D polytope; pitch classes are mapped onto the vertices of this polytope. Close harmonic relations such as fifths and thirds appear as small Euclidian distances. We calculate the Euclidian distance between analysis frames n + 1 and n − 1 to develop a harmonic change measure for frame n. A peak in the detection function denotes a transition from one harmonically stable region to another. Initial experiments show that the algorithm can successfully detect harmonic changes such as chord boundaries in polyphonic audio recordings.

This paper presents two low-cost, real-time methods for performance tracking on the violin. Low-latency pitch detection is achieved by using finger position measurements from a resistive fingerboard to inform audio analysis; the combination outperforming audio-only methods. Bow position and pressure are tracked using four optical reflectance sensors placed on the bow stick, allowing the displacement of the hair to be measured under the force of the string. Both sensor arrangements for this system can be fitted to existing violins without damaging the instrument. A case study demonstrating the utility of these techniques is presented finding fingered and bowed note onsets during performance.

This paper proposes a technique that estimates the locations along the string of the plucking event and the magnetic pickup of an electric guitar based on the autocorrelation of the spectral peaks. To improve accuracy, a method is introduced to flatten the spectrum before applying the autocorrelation function to the spectral peaks. The minimum mean squared error between the autocorrelation of the observed data and the electric guitar model is found in order to estimate the model parameters. The accuracy of the algorithm is tested on various plucking positions on all open strings for each pickup configuration. The accuracy of the proposed method for various plucking dynamics and fret positions is also evaluated. The method yields accurate results: the average absolute errors of the pickup position and plucking point estimates for single pickups are 3.53 and 5.11 mm, respectively, and for mixed pickups are 8.47 and 9.95 mm, respectively. The model can reliably distinguish which pickup configuration is selected using the pickup position estimates. Moreover, the method is robust to changes in plucking dynamics and fret positions.

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This paper proposes a technique that estimates the locations along the string of the plucking event and the magnetic pickup of an electric guitar based on the autocorrelation of the spectral peaks. To improve accuracy, a method is introduced to flatten the spectrum before applying the autocorrelation function to the spectral peaks. The minimum mean squared error between the autocorrelation of the observed data and the electric guitar model is found in order to estimate the model parameters. The accuracy of the algorithm is tested on various plucking positions on all open strings for each pickup configuration. The accuracy of the proposed method for various plucking dynamics and fret positions is also evaluated. The method yields accurate results: the average absolute errors of the pickup position and plucking point estimates for single pickups are 3.53 and 5.11 mm, respectively, and for mixed pickups are 8.47 and 9.95 mm, respectively. The model can reliably distinguish which pickup configuration is selected using the pickup position estimates. Moreover, the method is robust to changes in plucking dynamics and fret positions.