A Hybrid Recurrent Neural Network For Music Transcription

Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., Garcez, A. & Dixon, S. (2014). A Hybrid Recurrent Neural Network For Music Transcription. CoRR, 14(11), p. 1623.

[img]
Preview
Text - Draft Version
Download (94kB) | Preview

Abstract

We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance. We use recurrent neural networks (RNNs) and their variants as music language models (MLMs) and present a generative architecture for combining these models with predictions from a frame level acoustic classifier. We also compare different neural network architectures for acoustic modeling. The proposed model computes a distribution over possible output sequences given the acoustic input signal and we present an algorithm for performing a global search for good candidate transcriptions. The performance of the proposed model is evaluated on piano music from the MAPS dataset and we observe that the proposed model consistently outperforms existing transcription methods.

Item Type: Article
Additional Information: Copyright authors 2014
Uncontrolled Keywords: Recurrent Neural Networks, Polyphonic Music Transcription, Music Language Models
Subjects: M Music and Books on Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/14248

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics