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- Romain Hennequin, Bertrand David, Roland Badeau
- 2011 IEEE International Conference on Acoustics…
- 2011

In this paper we present a new technique for monaural source separation in musical mixtures, which uses the knowledge of the musical score. This information is used to initialize an algorithm which computes a parametric decomposition of the spectrogram based on non-negative matrix factorization (NMF). This algorithm provides time-frequency masks which are… (More)

This paper presents a new method to decompose musical spectrograms derived from Non-negative Matrix Factorization (NMF). This method uses time-varying harmonic templates (atoms) which are parametric: these atoms correspond to musical notes. Templates are synthesized from the values of the parameters which are learnt in an NMF framework. This… (More)

In a number of vibration applications, systems under study are slightly non-linear. It is thus of great importance to have a way to model and to measure these non-linearities in the frequency range of use. Cascade of Hammerstein models conveniently allows one to describe a large class of non-linearities. A simple method based on a phase property of… (More)

- Romain Hennequin, Roland Badeau, Bertrand David
- IEEE Transactions on Audio, Speech, and Language…
- 2011

Real-world sounds often exhibit time-varying spectral shapes, as observed in the spectrogram of a harpsichord tone or that of a transition between two pronounced vowels. Whereas the standard non-negative matrix factorization (NMF) assumes fixed spectral atoms, an extension is proposed where the temporal activations (coefficients of the decomposition on the… (More)

- Romain Hennequin, Roland Badeau, Bertrand David
- 2010 IEEE International Conference on Acoustics…
- 2010

Real world sounds often exhibit non-stationary spectral characteristics such as those produced by a harpsichord or a guitar. The classical Non-negative Matrix Factorization (NMF) needs a number of atoms to accurately decompose the spectrogram of such sounds. An extension of NMF is proposed hereafter which includes time-frequency activations based on ARMA… (More)

- Simon Leglaive, Romain Hennequin, Roland Badeau
- 2015 IEEE International Conference on Acoustics…
- 2015

In this paper, we propose a new method for singing voice detection based on a Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Network (RNN). This classifier is able to take a past and future temporal context into account to decide on the presence/absence of singing voice, thus using the inherent sequential aspect of a short-term feature… (More)

- Romain Hennequin, Roland Badeau, Bertrand David
- 2011 IEEE Workshop on Applications of Signal…
- 2011

In this paper, we present a new method for decomposing musical spectrograms. This method is similar to shift-invariant Probabilistic Latent Component Analysis, but, when the latter works with constant Q spectrograms (i.e. with a logarithmic frequency resolution), our technique is designed to decompose standard short time Fourier transform spectrograms (i.e.… (More)

Audio rendering systems are always slightly nonlinear. Their non-linearities must be modeled and measured for quality evaluation and control purposes. Cascade of Hammerstein models describes a large class of non-linearities. To identify the elements of such a model, a method based on a phase property of exponential sine sweeps is proposed. A complete model… (More)

- Romain Hennequin, Bertrand David, Roland Badeau
- IEEE Signal Processing Letters
- 2011

In this paper, we present a complete proof that the β-divergence is a particular case of Bregman divergence. This little-known result makes it possible to straightforwardly apply theorems about Bregman divergences to β-divergences. This is of interest for numerous applications since these divergences are widely used, for instance in… (More)

- Xabier Jaureguiberry, Gaël Richard, Pierre Leveau, Romain Hennequin, Emmanuel Vincent
- 2013 IEEE International Workshop on Machine…
- 2013

We propose in this paper a simple fusion framework for un-derdetermined audio source separation. This framework can be applied to a wide variety of source separation algorithms providing that they estimate time-frequency masks. Fusion principles have been successfully implemented for classification tasks. Although it is similar to classification, audio… (More)