By Topic

Transcription of polyphonic piano music with neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Marolt, M. ; Fac. of Comput. & Inf. Sci., Ljubljana Univ., Slovenia

This paper presents our experiences in building a system for transcription of polyphonic piano music. By transcription we mean the conversion of an audio recording of a polyphonic piano performance to a series of notes and their starting times. Our final goal is to build a transcription system that would transcribe polyphonic piano music over the entire piano range and with large polyphony. The system consists of three main stages. We first use a cochlear model based on the gammatone filterbank to transform an audio signal of a piano performance into time-frequency space. In the second stage we use a network of coupled adaptive oscillators to extract partial tracks from the output of the cochlear model and in the third stage we employ artificial neural networks acting as pattern recognisers to extract notes from the output of the oscillator network. The system uses several networks each trained to recognize the occurrence of a specific note in the input signal.

Published in:

Electrotechnical Conference, 2000. MELECON 2000. 10th Mediterranean  (Volume:2 )

Date of Conference: