Isolated guitar transcription using a deep belief network

Isolated guitar transcription using a deep belief network

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Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music.Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians.In response, several algorithms have been proposed to automatically analyze and transcribe the notes tenga flip orb sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously.

This paper presents a polyphonic transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar.The transcription system consists of a novel note pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each analysis frame of the input audio signal.Using a compiled dataset here of synthesized guitar recordings for evaluation, the algorithm described in this work results in an 11% increase in the f-measure of note transcriptions relative to Zhou et al.

’s (2009) transcription algorithm in the literature.This paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription.