- Recherche et sélection de publications
|
Projection-based demixing of spatial audio
- Derry Fitzgerald #1, Antoine Liutkus #2, Roland Badeau #3
-
| #1 |
NIMBUS Centre [Cork] (NIMBUS)
- Cork Institute of Technology
|
| #2 |
INRIA Nancy - Grand Est
|
| #3 |
Laboratoire Traitement et Communication de l'Information [Paris] (LTCI)
- Télécom ParisTech
- CNRS : UMR5141
|
- References
- IEEE Transactions on Audio, Speech and Language Processing, September 2016, vol. 24, n° 9, pp. 1560--1572
- Abstract
We propose a method to unmix multichannel audio signals into their different constitutive spatial objects. To achieve this, we characterize an audio object through both a spatial and a spectro-temporal modelling. The particularity of the spatial model we pick is that it neither assumes an object has only one underlying source point, nor does it attempt to model the complex room acoustics. Instead, it focuses on a listener perspective, and takes each object as the superposition of many contributions with different incoming directions and inter-channel delays. Our spectro-temporal probabilistic model is based on the recently proposed α-harmonisable processes, which are adequate for signals with large dynamics, such as audio. Then, the main originality of this work is to provide a new way to estimate and exploit inter-channel dependences of an object for the purpose of demixing. In the Gaussian α = 2 case, previous research focused on covariance structures. This approach is no longer valid for α < 2 where covariances are not defined. Instead, we show how simple linear combinations of the mixture channels can be used to learn the model parameters, and the method we propose consists in pooling the estimates based on many projections to correctly account for the original multichannel audio. Intuitively, each such downmix of the mixture provides a new perspective where some objects are cancelled or enhanced. Finally, we also explain how to recover the different spatial audio objects when all parameters have been computed. Performance of the method is illustrated on the separation of stereophonic music signals.
- Keywords
- source separation, probabilistic models, non-negative matrix factorization, music processing, musical source separation
- Category
- Article in peer reviewed Journal
- Research Area(s)
- Engineering Sciences/Signal and Image processing
- Identifier(s)
-
Bibliographic key DF:TASLP-16
- File(s)
-
- Export
-
- Last update
- on august 02, 2016
|
