Limits on echo return loss enhancement on a voice coded speech signal

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.

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An Improved Echo Cancellation Algorithm with Low Computational Complexity

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.2042 ◽

2013 ◽

Vol 303-306 ◽

pp. 2042-2045

Author(s):

Ya Ting Wu ◽

Y.Y. Zhao ◽

Fei Yu

Keyword(s):

Return Loss ◽

Linear Prediction ◽

Low Complexity ◽

Echo Cancellation ◽

Echo Canceller ◽

Convergence Process ◽

Speed Up ◽

Echo Return Loss Enhancement ◽

Active Detection ◽

The Voice

A low-complexity echo canceller integrated with vocoder is proposed in this paper to speed up the convergence process. By making full use of the linear prediction parameters retrieved from decoder and the voice active detection feature of the vocoder, the new echo canceller avoids the need to calculate decorrelation filter coefficients and prewhiten the received signal separately. Simulation results show performance improvement of the proposed algorithm in terms of convergence rate and echo return loss enhancement.

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The Dynamic LMS for Line Echo Cancellation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2941 ◽

2013 ◽

Vol 284-287 ◽

pp. 2941-2945

Author(s):

Ning Yun Ku ◽

Shaw Hwa Hwang ◽

Shun Chieh Chang ◽

Cheng Yu Yeh

Keyword(s):

Impulse Response ◽

Return Loss ◽

Experimental Results ◽

Echo Cancellation ◽

Mean Square ◽

Least Mean Square ◽

Excellent Performance ◽

Pure Delay ◽

Echo Return Loss Enhancement ◽

Computation Load

To the best of our knowledge, this study represents the proposal using the dynamic least mean square (DLMS) algorithm to reduce the computation load of LMS. Moreover, three regions of impulse response of line echo path are also proposed to analyze the redundant coefficients. Using the DLMS method, redundant coefficients can be detected and grouped, thereby automatically reducing computation. We employed line echo cancellation (LEC) to evaluate the performance of DLMS. The pure-delay and overlong regions of impulse response of line echo path are grouped and the associated computation load is reduced. The experimental results confirm the excellent performance of DLMS achieving a 35% savings in computation. Moreover, the quality echo return loss enhancement (ERLE) of DLMS also maintains at a level nearly equal to LMS.

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Perceptual Learning of Vocoded Speech With and Without Contralateral Hearing: Implications for Cochlear Implant Rehabilitation

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-20-00385 ◽

2020 ◽

pp. 1-10

Author(s):

Martin Chavant ◽

Alexis Hervais-Adelman ◽

Olivier Macherey

Keyword(s):

Cochlear Implant ◽

Perceptual Learning ◽

Speech Signal ◽

Training Phase ◽

Monosyllabic Words ◽

Low Pass ◽

Contralateral Ear ◽

Number Of Individuals ◽

Insight Into ◽

Vocoded Speech

Purpose An increasing number of individuals with residual or even normal contralateral hearing are being considered for cochlear implantation. It remains unknown whether the presence of contralateral hearing is beneficial or detrimental to their perceptual learning of cochlear implant (CI)–processed speech. The aim of this experiment was to provide a first insight into this question using acoustic simulations of CI processing. Method Sixty normal-hearing listeners took part in an auditory perceptual learning experiment. Each subject was randomly assigned to one of three groups of 20 referred to as NORMAL, LOWPASS, and NOTHING. The experiment consisted of two test phases separated by a training phase. In the test phases, all subjects were tested on recognition of monosyllabic words passed through a six-channel “PSHC” vocoder presented to a single ear. In the training phase, which consisted of listening to a 25-min audio book, all subjects were also presented with the same vocoded speech in one ear but the signal they received in their other ear differed across groups. The NORMAL group was presented with the unprocessed speech signal, the LOWPASS group with a low-pass filtered version of the speech signal, and the NOTHING group with no sound at all. Results The improvement in speech scores following training was significantly smaller for the NORMAL than for the LOWPASS and NOTHING groups. Conclusions This study suggests that the presentation of normal speech in the contralateral ear reduces or slows down perceptual learning of vocoded speech but that an unintelligible low-pass filtered contralateral signal does not have this effect. Potential implications for the rehabilitation of CI patients with partial or full contralateral hearing are discussed.

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“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

Perspectives on Speech Science and Orofacial Disorders ◽

10.1044/ssod21.2.44 ◽

2011 ◽

Vol 21 (2) ◽

pp. 44-54

Author(s):

Kerry Callahan Mandulak

Keyword(s):

Speech Production ◽

Speech Signal ◽

Clinical Utility ◽

Acoustic Analysis ◽

Moment Analysis ◽

Analysis Tool ◽

Spectral Moment ◽

Clinical Measure ◽

Perceptual Analysis ◽

Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

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Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

Izmeritel`naya Tekhnika ◽

10.32446/0368-1025it.2020-11-65-72 ◽

2020 ◽

pp. 65-72

Author(s):

V. V. Savchenko ◽

A. V. Savchenko

Keyword(s):

Speech Signal ◽

Communication Channel ◽

Speaker Identification ◽

A Priori ◽

Small Samples ◽

Practical Implementation ◽

Identification System ◽

New Information ◽

Discrimination Criterion ◽

A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

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