Deep neural networks (DNNs) are often used to tackle the single channel source separation (SCSS) problem by predicting time-frequency masks. The predicted masks are then used to separate the sources from the mixed signal. Different types of masks produce separated sources with different levels of distortion and interference. Some types of masks produce separated sources with low distortion, while other masks produce low interference between the separated sources. In this paper a combination of different DNNs’ predictions (masks) is used for SCSS to achieve better quality of the separated sources than using each DNN individually. We train four different DNNs by minimizing four different cost functions to predict four different masks. The first and second DNNs are trained to approximate reference binary and soft masks. The third DNN is trained to predict a mask from the reference sources directly. The last DNN is trained similarly to the third DNN but with an additional discriminative constraint to maximize the differences between the estimated sources. Our experimental results show that combining the predictions of different DNNs achieves separated sources with better quality than using each DNN individually.
|Title of host publication||Audio Engineering Society Convention 140|
|Publisher||Audio Engineering Society|
|Publication status||Published - 26 May 2016|
|Event||140th Audio Engineering Society Convention 2016 - Palais des Congrès, Paris, France|
Duration: 4 Jun 2016 → 7 Jun 2016
Conference number: 140
http://www.aes.org/events/140/ (Link to Conference Website)
|Conference||140th Audio Engineering Society Convention 2016|
|Period||4/06/16 → 7/06/16|
Grais, E. M., Roma, G., Simpson, A. JR., & Plumbley, M. D. (2016). Single-Channel Audio Source Separation Using Deep Neural Network Ensembles. In Audio Engineering Society Convention 140  Audio Engineering Society.