NDLI: Improving the performance of the PNLMS algorithm using $l

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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 24

Issue 9, September 2016

Issue 8, August 2016

Issue 7, July 2016

A simplified subband ANC algorithm without secondary path modeling

Multiple non-negative matrix factorization for many-to-many voice conversion

Training deep bidirectional LSTM acoustic model for LVCSR by a context-sensitive-chunk BPTT approach

Text-dependent speaker recognition with random digit strings

Foreground speech segmentation and enhancement using glottal closure instants and mel cepstral coefficients

Robust estimation and tracking of pitch period using an efficient Bayesian filter

A novel method of glottal inverse filtering

Multi-frame amplitude envelope estimation for modification of singing voice

Improving trajectory modelling for DNN-based speech synthesis by using stacked bottleneck features and minimum generation error training

Fusion methods for speech enhancement and audio source separation

Improving the performance of the PNLMS algorithm using l₁ norm regularization

Spotforming: spatial filtering with distributed arrays for position-selective sound acquisition

Learning the multilingual translation representations for question retrieval in community question answering via non-negative matrix factorization

Power-normalized cepstral coefficients (PNCC) for robust speech recognition

Issue 2, February 2016

Issue 1, January 2016

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 23

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) : Volume 22

Improving the performance of the PNLMS algorithm using $l_{1}$ norm regularization

Content Provider	ACM Digital Library
Author	Chakraborty, Mrityunjoy Das, Rajib Lochan
Copyright Year	2016
Abstract	The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In this paper, we address this problem by introducing a carefully constructed $l_{1}$ norm (of the coefficients) penalty in the PNLMS cost function which favors sparsity. This results in certain zero attracting terms in the PNLMS weight update equation which help in the shrinkage of the coefficients, especially the inactive taps, thereby arresting the slowing down of convergence and also producing lesser steady state excess mean square error (EMSE). A rigorous convergence analysis of the proposed algorithm is presented that expresses the steady state mean square deviation of both the active and the inactive taps in terms of a zero attracting coefficient of the algorithm. The analysis reveals that further reduction of the EMSE is possible by deploying a variable step size (VSS) simultaneously with a variable zero attracting coefficient in the weight update process. Simulation results confirm superior performance of the proposed VSS zero attracting PNLMS algorithm over existing algorithms, especially in terms of having both higher convergence speed and lesser steady state EMSE simultaneously.
Starting Page	1280
Ending Page	1290
Page Count	11
File Format	PDF
ISSN	23299290
e-ISSN	23299304
Volume Number	24
Issue Number	7
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	PNLMS algorithm Mean square deviation (msd) Sparse systems Variable step size Zero attractors
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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A variable step-size zero attracting proportionate normalized least mean square algorithm

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A new local polynomial modeling-based variable forgetting factor RLS algorithm and its acoustic applications

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Adaptive Feedback Cancellation for Realistic Hearing Aid Applications

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Improving the performance of the PNLMS algorithm using $l_{1}$ norm regularization

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