Jun 18, 2007 · We introduce, two methods based on the sparsity assumption of the sources in the time-frequency (TF) domain. The first one assumes that the ...

We introduce, two methods based on the sparsity assumption of the sources in the time–frequency (TF) domain. The first one assumes that the sources are disjoint ...

Apr 20, 2018 · This concept is a natural extension of both the time domain and the frequency domain processing that involves representing signals in a two- ...

We introduce, two methods based on the sparsity assumption of the sources in the time-frequency (TF) domain. The first one assumes that the sources are disjoint ...

We introduce, two methods based on the sparsity assumption of the sources in the time-frequency (TF) domain. The first one assumes that the sources are disjoint ...

This paper focuses on the separation for time–frequency (TF) overlapped communication signals received by the sensors. A novel blind separation strategy is ...

The main focus of this paper is the separation of underdetermined convolutive blind speech in a multi-speaker environment. We present a method based on mask ...

In this paper all the signals in the time domain are represented by small letters whereas signals in the frequency domain are represented by capital letters.

In this paper, to solve the permutation alignment and obtain better source separation results, we exploit the algebraic structure of tensor factorization model ...

In many real-world applications these limitations are too restrictive. We propose a method for underdetermined blind source separation of convolutive mixtures.