Abstract
Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate ‘ideal’ binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for ‘karaoke’ type applications.
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Acknowledgment
AJRS, GR and MDP were supported by grant EP/L027119/1 from the UK Engineering and Physical Sciences Research Council (EPSRC). Data and materials are available at doi:10.15126/surreydata.00807909.
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Simpson, A.J.R., Roma, G., Plumbley, M.D. (2015). Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_50
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DOI: https://doi.org/10.1007/978-3-319-22482-4_50
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