NDLI: Performance evaluation of single channel speech separation using non-negative matrix factorization

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2014 IEEE National Conference on Communication, Signal Processing and Networking (NCCSN)

Table of contents

Automated pseudo-coloring of grayscale images based on contourlet transform

Comparison of conventional methods and deep belief networks for isolated word recognition

Sentence segmentation for speech processing

Sensitive keyword spotting for crime analysis

Feature selection, optimization and performance analysis of classifiers for biological images

Online handwritten malayalam character recognition using LIBSVM in matlab

A 8-bit SAR ADC using current mode approach for bio-medical applications

Performance evaluation of energy based spectrum sensing in multipath fading channel for cognitive radio system

A hexagonal boundary fractal antenna with WiMAX band rejection

Primary user signal detection in cognitive radio networks using cyclostationary feature analysis

Biometric privacy using visual cryptography, halftoning and watermarking for multiple secrets

A power allocation technique for MIMO-OFDM based cognitive radio with improved energy efficiency

Design and implementation of compressive samplers

Performance evaluation of single channel speech separation using non-negative matrix factorization

A local neural network approach to detect actions of human body

Performance analysis of PLNC based cognitive radio relay systems with two source and destination nodes

Patch antenna for wireless LAN systems with polarization reconfigurability

FPGA implementation of RC-4T and WPA

Performance of gravitational search algorithm in wireless sensor network localization

A novel X band-notch ultra wideband antenna with inverted T-shape conductor

Outage analysis of OFDM based full duplex cognitive DF relay network in the presence of narrowband interference

Author index

Performance evaluation of single channel speech separation using non-negative matrix factorization

Content Provider	IEEE Xplore Digital Library
Author	Nandakumar, M.M. Bijoy, K.E.
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. & Commun. Eng., MES Coll. of Eng., Kuttippuram, India (Nandakumar, M.M.; Bijoy, K.E.)
Abstract	Blind Source Separation (BSS) of underdetermined mixture has acquired a huge attention in signal processing environment, even though it is very much difficult to separate the underlying sources. The difficulty in source separation arise due to the mixing of large number of source signals in time and frequency, and propagation of it to one or more sensors through air. The objective in BSS is to identify the underlying source signals based on measurements of the mixed sources. Among many of the techniques used in BSS, due to its direct, easy to code and intuitive interpretability of basis and activation components, NMF provide an accurate form of parts-based representation of underlying data. Even though both supervised and unsupervised modes of operations are used in NMF, supervised mode performs well due to the use of pre-learned basis vectors corresponding to each underlying source. In this paper two of the multiplicative algorithms, Regularized Expectation Minimization Maximum Likelihood Algorithm (REMML) and Regularized Image Space Reconstruction Algorithm (RISRA) with sparseness constraint are taken to evaluate the performance of BSS. By the use of speech and music mixtures, Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR) and Signal to Artifact Ratio (SAR) are evaluated.
Starting Page	1
Ending Page	4
File Size	278187
Page Count	4
File Format	PDF
ISBN	9781479966462
DOI	10.1109/NCCSN.2014.7001159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Blind Source Separation Source separation NMF EMML Vectors Sparse matrices BSS Evaluation Source Separation Speech Cost function ISRA Principal component analysis
Content Type	Text
Resource Type	Article

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Adaptive Sparsity Non-Negative Matrix Factorization for Single-Channel Source Separation

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Speech/music separation using non-negative matrix factorization with combination of cost functions

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Discriminative non-negative matrix factorization for single-channel speech separation

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Performance evaluation of single channel speech separation using non-negative matrix factorization

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