Improving the Plucking Point Position Estimation in a Classical Guitar Performance using a Novel Video-Based Approach

The plucking point position is an important gestural parameter that controls the tone produced in a classical guitar. Existing studies used a theoretical model of the plucking point position to estimate the parameter from audio signals. However, in performances where the classical guitarist evokes different tonal characteristics through varying gestural parameters, a theoretical model may not suffice. In this work, we explored the use of a novel video-based approach in estimating the parameter and compared it to the more common audio-based approach. We collected a data set consisting of real classical guitar performances from a classical guitarist performing with his plucking nails as the medium of excitation. We have discovered that on our data set, the video-based approach, which had an average absolute error of 1.0656 cm, outperformed the audio-based approach, which had an average absolute error of 2.5904 cm, in terms of the estimation accuracy. In terms of the speed however, the audio-based approach had a faster estimation time (13.63 milliseconds) on average compared to the video-based approach (176.96 milliseconds). Hence, we recommend the video-based approach for applications where the estimation accuracy is critical, especially on performances with varying tonal characteristics due to different gestural parameters. Meanwhile, we recommend the audio-based approach in real-time applications.