Combined Audio and Video Analysis for Guitar Chord Identification

This research presents a multi-modal approach to automatically identifying guitar chords using audio and video of the performer. Chord identification is for stringed instruments adds extra ambiguity as a single chord or melody may be played in different positions on the fretboard. Preserving this information is important, because it signifies the original fingering, and implied “easiest” way to perform the selection. This chord identification system combines analysis of audio to determine the general chord scale (i.e. A major, G minor), and video of the guitarist to determine chord voicing (i.e. open, barred, inversion), to accurately identify the guitar chord

Combined audio and video analysis for guitar chord identification

This thesis presents a multi-modal approach to automatically identifying guitar chords using audio and video of the performer. Chord identi cation is typically performed by analyzing the audio, using a chroma based feature to extract pitch class information, then identifying the chord with the appropriate label. Even if this method proves perfectly accurate, stringed instruments add extra ambiguity as a single chord or melody may be played in di erent positions on the fretboard. Preserving this information is important, because it signi es the original ngering, and implied \easiest" way to perform the selection. This chord identi cation system combines analysis of audio to determine the general chord scale (i.e. A major, G minor), and video of the guitarist to determine chord voicing (i.e. open, barred, inversion), to accurately identify the guitar chord.M.S., Electrical Engineering -- Drexel University, 201