Identification and extraction of singing voice from within musical mixtures is a key challenge
in source separation and machine audition. Recently, deep neural networks (DNN) have …

This paper deals with the extraction of an instrument from music by using a deep neural
network. As prior information, we only assume to know the instrument types that are present …

In this paper, a novel approach for single channel source separation (SCSS) using a deep
neural network (DNN) architecture is introduced. Unlike previous studies in which DNN and …

Abstract We propose Deep Feature Factorization (DFF), a method capable of localizing
similar semantic concepts within an image or a set of images. We use DFF to gain insight …

We explore the possibility of leveraging accelerometer data to perform speech enhancement
in very noisy conditions. Although it is possible to only partially reconstruct user's speech …

The ideal binary mask (IBM) is widely considered to be the benchmark for time–frequency-
based sound source separation techniques such as computational auditory scene analysis …

Non-negative matrix factorization (NMF) is one of the most well-known techniques that are
applied to separate a desired source from mixture data. In the NMF framework, a collection …

In this paper we propose a Deep Neural Network (D NN) based Speech Enhancement (SE)
system that is designed to maximize an approximation of the Short-Time Objective …

In recent research, deep neural network (DNN) has been used to solve the monaural source
separation problem. According to the training objectives, DNN-based monaural speech …

This paper presents a review on different methodologies adopted in speech enhancement
and the role of Deep Neural Networks (DNN) in enhancement of speech. Mostly, a speech …