Títol:
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Monoaural audio source separation using deep convolutional neural networks
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Autor/a:
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Chandna, Pritish; Miron, Marius; Janer Mestres, Jordi; Gómez Gutiérrez, Emilia, 1975-
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Abstract:
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Comunicació presentada a 13th International Conference on Latent Variable Analysis and Signal Separation, celebrada a Grenoble (França) els dies 21 a 23 de febrer de 2017. |
Abstract:
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In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results. |
Abstract:
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This work is partially supported by the Spanish Ministry of Economy and Competitiveness under CASAS project (TIN2015-70816-R). |
Matèries:
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-Convolutional autoencoder -Music source separation -Deep learning -Convolutional neural networks -Low-latency |
Drets:
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© Springer The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-53547-0_25 |
Tipus de document:
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Objecte de conferència Article - Versió acceptada |
Publicat per:
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Springer
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