![]() ![]() ![]() As an additional contribution, we analyze the influence of the note duration characteristics in the classification performance. Our results show that the proposed system achieves accuracy values above 0.88 for the estimation of the plucking style, expression style, and string number for isolated note samples. Using a Support Vector Machine (SVM) classifier, we automatically classify the instrument-level parameters for each detected note event. Then, we model the spectral envelope of each note and derive various timbre-related audio features. Our approach is to first apply a note onset detection followed by a tracking of the fundamental frequency contours based on a reassigned magnitude spectrogram. As a second step, we aim to automatically detect the applied plucking and expression style as well as the fret and string positions for each note (instrument-level parameters). Our goal is to first develop a system for a robust estimation of the note parameters pitch, onset, and duration (score-level parameters). This paper deals with the automatic transcription of solo bass guitar recordings with an additional estimation of playing techniques and fretboard positions used by the musician.
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