TY - GEN
T1 - Unsupervised Pretraining of Echo State Networks for Onset Detection
AU - Steiner, Peter
AU - Jalalvand, Azarakhsh
AU - Birkholz, Peter
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Clustering
KW - Echo State Networks
KW - Note onset detection
UR - http://www.scopus.com/inward/record.url?scp=85115665352&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-86383-8_5
DO - 10.1007/978-3-030-86383-8_5
M3 - Conference contribution
AN - SCOPUS:85115665352
SN - 9783030863821
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 70
BT - Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
A2 - Farkaš, Igor
A2 - Masulli, Paolo
A2 - Otte, Sebastian
A2 - Wermter, Stefan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 30th International Conference on Artificial Neural Networks, ICANN 2021
Y2 - 14 September 2021 through 17 September 2021
ER -