Classical drug design methodologies are hugely costly and time-consuming, with
approximately 85% of the new proposed molecules failing in the first three phases of the …

Nonnegative matrix factorization (NMF) is an emerging tool for meaningful low-rank matrix
representation. In NMF, explicit constraints are usually required, such that NMF generates …

Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-
dimensional feature representation of relational data. Currently, NMTF learns a …

This paper studies a new feature selection method for data classification that efficiently
combines the discriminative capability of features with the ridge regression model. It first sets …

We show that a Modular Neural Network (MNN) can combine various speech enhancement
modules, each of which is a Deep Neural Network (DNN) specialized on a particular …

Non-negative matrix factorization (NMF) has been widely applied in information retrieval and
computer vision. However, its performance has been restricted due to its limited tolerance to …

In this article, we propose a new source separation method in which the dual-tree complex
wavelet transform (DTCWT) and short-time Fourier transform (STFT) algorithms are used …

While dynamics is an important characteristic in music performance, it has been rarely
researched in automatic music transcription. We propose a method to estimate individual …

Separating speech is a challenging area of research, especially when trying to separate the
desired source from its combination. Deep learning has arisen as a promising solution …

Speech is essential to human communication; therefore, distinguishing it from noise is
crucial. Speech separation becomes challenging in real-world circumstances with …