Instrument-Centered Music Transcription of Solo Bass Guitar Recordings

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. Our goal is to first develop a system for a robust estimation of the note parameters pitch, onset, and duration (score-level parameters). 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 approach is to first apply a note onset detection followed by a tracking of the fundamental frequency contours based on a reassigned magnitude spectrogram. Then, we model the spectral envelope of each note and derive various timbre-related audio features. Using a support vector machine classifier, we automatically classify the instrument-level parameters for each detected note event. 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. As an additional contribution, we analyze the influence of the note duration characteristics in the classification performance. In a score-level evaluation on a novel public dataset of solo bass guitar tracks, our method outperforms three existing transcription algorithms for bass transcription in polyphonic music as well as a melody transcription algorithm for monophonic music.