A novel single‐channel blind source separation method is studied. This method not only
addresses the problem that the traditional blind source separation method depends on the …

This paper proposes a new system for the unsupervised diagnostic and monitoring of
defects in waveguide imaging. The proposed method is automatic and does not require …

A novel single-channel source separation method is presented to recover the original
signals given only a single observed mixture in noisy environment. The proposed separation …

An unsupervised machine learning algorithm based on nonnegative matrix factor Two-
dimensional deconvolution (NMF2D) with approximated optimum model order is proposed …

Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine
learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust …

This paper proposes a solution for events classification from a sole noisy mixture that consist
of two major steps: a sound-event separation and a sound-event classification. The …

In this paper, an optimized complex nonnegative tensor factor 2D deconvolution (CNTF2D)
is proposed to separate the sources that have been mixed in an underdetermined …

A novel approach for solving the single-channel signal separation is presented the
proposed sparse nonnegative tensor factorization under the framework of maximum a …

The processing of complex-valued signals through neural networks is the important and
challenging fields in image processing and digital signal processing. The networks with …

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly
time-varying signals especially when they are overlapped in time and frequency plane. In …