236 research outputs found
Supervised Group Nonnegative Matrix Factorisation With Similarity Constraints And Applications To Speaker Identification
International audienceThis paper presents supervised feature learning approaches for speaker identification that rely on nonnegative matrix factorisa-tion. Recent studies have shown that group nonnegative matrix factorisation and task-driven supervised dictionary learning can help performing effective feature learning for audio classification problems. This paper proposes to integrate a recent method that relies on group nonnegative matrix factorisation into a task-driven supervised framework for speaker identification. The goal is to capture both the speaker variability and the session variability while exploiting the discriminative learning aspect of the task-driven approach. Results on a subset of the ESTER corpus prove that the proposed approach can be competitive with I-vectors. Index Terms— Nonnegative matrix factorisation, feature learning , dictionary learning, online learning, speaker identificatio
Bayesian Inference for Nonnegative Matrix Factorisation Models
We describe nonnegative matrix factorisation (NMF) with a Kullback-Leibler (KL) error measure in a statistical
framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior
leads to the standard KL-NMF algorithms as special cases, where maximum likelihood parameter estimation is carried
out via the Expectation-Maximisation (EM) algorithm. Starting from this view, we develop full Bayesian inference
via variational Bayes or Monte Carlo. Our construction retains conjugacy and enables us to develop more powerful
models while retaining attractive features of standard NMF such as monotonic convergence and easy implementation.
We illustrate our approach on model order selection and image reconstruction
Partial Envelope Analysis with Nonnegative Matrix Factorization
This paper investigates the application of the technique of Nonnegative Matrix Factorisation (NMF) to the analysis of partial envelopes extracted from a variety of instrumental sounds. Firstly, the initialisation of the NMF method is shown to be important for obtaining useful results. This is followed by an experimental analysis of the bases produced by NMF that demonstrates how they represent prototypical signal
features
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
In this paper, we study the trade-offs of different inference approaches for
Bayesian matrix factorisation methods, which are commonly used for predicting
missing values, and for finding patterns in the data. In particular, we
consider Bayesian nonnegative variants of matrix factorisation and
tri-factorisation, and compare non-probabilistic inference, Gibbs sampling,
variational Bayesian inference, and a maximum-a-posteriori approach. The
variational approach is new for the Bayesian nonnegative models. We compare
their convergence, and robustness to noise and sparsity of the data, on both
synthetic and real-world datasets. Furthermore, we extend the models with the
Bayesian automatic relevance determination prior, allowing the models to
perform automatic model selection, and demonstrate its efficiency
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability
Blind source separation is a common processing tool to analyse the
constitution of pixels of hyperspectral images. Such methods usually suppose
that pure pixel spectra (endmembers) are the same in all the image for each
class of materials. In the framework of remote sensing, such an assumption is
no more valid in the presence of intra-class variabilities due to illumination
conditions, weathering, slight variations of the pure materials, etc... In this
paper, we first describe the results of investigations highlighting intra-class
variability measured in real images. Considering these results, a new
formulation of the linear mixing model is presented leading to two new methods.
Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation
method based on the assumption of a linear mixing model, which can deal with
intra-class variability. To overcome UP-NMF limitations an extended method is
proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each
sensed spectrum, these extended versions of NMF extract a corresponding set of
source spectra. A constraint is set to limit the spreading of each source's
estimates in IP-NMF. The methods are tested on a semi-synthetic data set built
with spectra extracted from a real hyperspectral image and then numerically
mixed. We thus demonstrate the interest of our methods for realistic source
variabilities. Finally, IP-NMF is tested on a real data set and it is shown to
yield better performance than state of the art methods
Unsupervised learning of overlapping image components using divisive input modulation
This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance
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