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This research proposes an approach for computing the time offsets between audio sequences that contain musical sounds from different instruments produced in a distributed way and which have a set of weak features that are not useful as alignment points. It is therefore necessary to apply transformations in order to find a set of distinctive features to compute the offset values in a suitable way. The main issue that occurs with such a system is nonlinearity that does not allow the delay to be predicted by using a linear function. To solve this problem, the authors propose a set of long short-term memory (LSTM) layers to create a neural network model capable of learning such features transformations in a supervised approach, using a gradient-descent optimizer. This demonstrates the use of a recurrence matrix to extract timing information from a set of transformed features given by the neural network output. With this approach, the algorithm can classify up to 60% of a specific combination from the MedleyDB data set, and reduce the search space to five possibilities with accuracy up to 90% while keeping the precision of 10 ms. This performance is equal or better than state-of-the-art methods.
Author (s): Pereira, Igor; Distante, Cosimo; Silveira, Luiz F.; Gonçalves, Luiz
Affiliation:
Institute of Applied Sciences and Intelligent Systems, Lecce, Italy; Federal University of Rio Grande do Norte, Natal, Brazil
(See document for exact affiliation information.)
Publication Date:
2020-03-06
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Pereira, Igor; Distante, Cosimo; Silveira, Luiz F.; Gonçalves, Luiz; 2020; Using Neural Networks to Compute Time Offsets from Musical Instruments [PDF]; Institute of Applied Sciences and Intelligent Systems, Lecce, Italy; Federal University of Rio Grande do Norte, Natal, Brazil; Paper ; Available from: https://aes2.org/publications/elibrary-page/?id=20726
Pereira, Igor; Distante, Cosimo; Silveira, Luiz F.; Gonçalves, Luiz; Using Neural Networks to Compute Time Offsets from Musical Instruments [PDF]; Institute of Applied Sciences and Intelligent Systems, Lecce, Italy; Federal University of Rio Grande do Norte, Natal, Brazil; Paper ; 2020 Available: https://aes2.org/publications/elibrary-page/?id=20726
@article{pereira2020using,
author={pereira igor and distante cosimo and silveira luiz f. and gonçalves luiz},
journal={journal of the audio engineering society},
title={using neural networks to compute time offsets from musical instruments},
year={2020},
volume={68},
issue={3},
pages={157-167},
month={march},}