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.
Original language | English |
---|---|
Title of host publication | Latent Variable Analysis and Signal Separation |
Editors | Emmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský |
Publisher | Springer, Cham |
Pages | 429-436 |
Number of pages | 8 |
ISBN (Electronic) | 9783319224824 |
ISBN (Print) | 9783319224817 |
DOIs | |
Publication status | Published - 15 Aug 2015 |
Externally published | Yes |
Event | 12th International Conference on Latent Variable Analysis and Signal Separation - Technical University of Liberec, Liberec, Czech Republic Duration: 25 Aug 2015 → 28 Aug 2015 Conference number: 12 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer |
Volume | 9237 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th International Conference on Latent Variable Analysis and Signal Separation |
---|---|
Abbreviated title | LVA/ICA 2015 |
Country | Czech Republic |
City | Liberec |
Period | 25/08/15 → 28/08/15 |