Isolated guitar transcription using a deep belief network

• Author(s) / Creator(s)

  • Burlet, G.
  • Hindle, Abram

• 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 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 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.

• Date created

  2017

• Subjects / Keywords

  • Deep learning
  • Music information retrieval
  • Instrument transcription

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-j6z5-6b62

• License

  Attribution 4.0 International