Independent Component Analysis is the best known method for solving blind source separation problems. In general, the number of sources must be known in advance. In many cases, pre...
Andreas Sandmair, Alam Zaib, Fernando Puente Le&oa...
Abstract. A new dictionary learning method for exact sparse representation is presented in this paper. As the dictionary learning methods often iteratively update the sparse coeffi...
In this paper, we propose a novel method for blind source separation (BSS) based on time-frequency sparseness (TF) that can estimate the number of sources and time-frequency masks,...
Abstract. We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguish...
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...
Abstract. In this work, we present a direction-of-arrival (DOA) estimation method for narrowband sources impinging from the far-field on a uniform linear array (ULA) of sensors, ba...
Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier...
Jonathan Le Roux, Emmanuel Vincent, Yuu Mizuno, Hi...
Visual tracking with moving cameras is a challenging task. The global motion induced by the moving camera moves the target object outside the expected search area, according to th...
This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to ...