Monoaural Audio Source Separation Using Deep Convolutional Neural Networks [chapter]

Pritish Chandna, Marius Miron, Jordi Janer, Emilia Gómez
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
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
more &raquo; ... sults and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-53547-0_25">doi:10.1007/978-3-319-53547-0_25</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k254yklw35f5phskmdu4avaniu">fatcat:k254yklw35f5phskmdu4avaniu</a> </span>
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