Unsupervised Pretraining of Echo State Networks for Onset Detection

Peter Steiner, Azarakhsh Jalalvand, Peter Birkholz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Note onset detection – the detection of the beginning of new note events – is a fundamental task for music analysis that can help to improve Automatic Music Transcription (AMT). The method for onset detection always follows a similar outline: An audio signal is transformed into an Onset Detection Function (ODF), which should have rather low values (i.e. close to zero) for most of the time, and pronounced peaks at onset times, which can then be extracted by applying peak picking algorithms on the ODF. Currently, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) define the state of the art. In this paper, we build upon previous work about onset detection using Echo State Networks (ESNs) that have achieved comparable results to CNNs. We show that unsupervised pre-training of the ESN leads to similar results whilst reducing the model complexity.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Pages59-70
Number of pages12
ISBN (Print)9783030863821
DOIs
StatePublished - 2021
Event30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online
Duration: Sep 14 2021Sep 17 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12895 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th International Conference on Artificial Neural Networks, ICANN 2021
CityVirtual, Online
Period9/14/219/17/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Clustering
  • Echo State Networks
  • Note onset detection

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