General purpose audio blind source separation algorithms have to deal with a large dynamic range for the different sources to be separated. In our algorithm the mixture is separated into single notes. These notes are clustered to construct the melodies played by the active sources. The non-negative matrix factorization (NMF) leads to good results in clustering the notes according to spectral fe...

In this work, we study Non-Negative Matrix Factorization (NMF) and compare standard algorithms with an extension to NMF of a Blind Source Separation algorithm using sparsity, Generalized Morphological Component Analysis (GMCA). We also develop a more robust version of GMCA handling more precisely the priors through sub-iterations, which we call rGMCA. We present preliminary results showing GMCA...

Non-negative matrix factorization (NMF) is an increasingly popular feature extraction method. Since it is designed to fit training samples using linear combination of non-negative basis vectors, it is particular suitable for image applications as it affords intuitive localized interpretations. However, in this space defined by the NMF basis images, there has not been any systematic research to ...

We demonstrate how non-negative matrix factorization (NMF) can be used to decompose the inter trial phase coherence (ITPC) of multi-channel EEG to yield a unique decomposition of time-frequency signatures present in various degrees in the recording channels. The NMF optimization is easily generalized to a parallel factor (PARAFAC) model to form a non-negative multi-way factorization (NMWF). Whi...

In order to efficiently extract the focused regions from the source images and improve the quality of the fused image, this paper presents a novel image fusion scheme with non-negative matrix factorization (NMF). The source images are fused by NMF to construct temporary fused image, whose region homogeneityis used to split the source images into regions.The focused regions are detected and inte...

This paper deals with audio source separation using supervised non-negative matrix factorization (NMF). We propose a prior model based on mixtures of Gamma distributions for each sound class, which hyperparameters are trained given a training corpus. This formulation allows adapting the spectral basis vectors of the sound sources during actual operation, when the exact characteristics of the so...

Non-negative matrix factorization (NMF) is a dimensionality reduction method that usually leads to a part-based representation, and it is shown to be effective for source separation. However, the performance of the source separation degrades when one signal can be described with the bases for the other source signals. In this paper, we propose a discriminative NMF (DNMF) algorithm which exploit...

In this paper, we propose a maximum-margin framework for classification using Nonnegative Matrix Factorization. In contrast to previous approaches where the classification and matrix factorization stages are separated, we incorporate the maximum margin constraints within the NMF formulation, i.e we solve for a base matrix that maximizes the margin of the classifier in the low dimensional featur...

Along with the growth of the Internet, automatic recommender systems have become popular. Due to being intuitive and useful, factorization based models, including the Nonnegative Matrix Factorization (NMF) model, are one of the most common approachs for building recommender systems. In this study, we focus on how a recommender system can be built for online services and how the parameters of an...

The Non-negative Matrix Factorization (NMF) is a popular technique for intelligent systems, which can be widely used to decompose nonnegative matrix into two factor matrices: basis and coefficient one, respectively. main objective of NMF ensure that the operation results matrices are as close original possible. Meanwhile, stability generalization ability algorithm should ensured. Therefore, per...