Noise impaired echo path modelling using chaotic coded speech

Elmirghani, Jaafar M. H.; Almehyas, A.M.; Milner, S.H.; Cryan, R.A.

doi:10.1109/glocom.1996.592002

Cited by 4 publications

(2 citation statements)

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“…Additionally, in Fig. 4, following initial adaptation using the chaotic coding regime, the curves saturate near the current SNR value as expected from the WNP analysis in [6], [7]. Using chaotic coding, for example at SNR= 50 dB, the FCLS results in a MMI of about -57 dB after 7000 iterations (0.875 s).…”

Section: B Adaptation Using Chaotic Coded Speechsupporting

confidence: 59%

“…Recently a growing interest in chaotic transmission regimes has been witnessed and in the communication area beside security aspects the interest is mainly rooted in the spectral properties of chaotic signals which makes them ideal as driving signals for LMS based estimation schemes. Moreover, chaotic coding regimes can be used to whiten the driving signal spectrum while transmitting the data [6], [7], [8]. Chaotic coding can be carried out using the LM where X is a data modulated bifurcation parame- Both algorithms yield good performance in this case since the autocorrelation matrix R and 9 in (7) become nearly diagonal resulting in a small eigenvalue spread and a good NLMS/FCLS performance as observed in Fig.…”

Section: B Adaptation Using Chaotic Coded Speechmentioning

confidence: 99%

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A novel approach to robust channel estimation and its performance with uncoded and chaotic coded speech

Müller

Elmirghani²

Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137)

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High &OS in communication demands channel estimation in real time. The normalized least mean squares (NLMS) algorithm is employed due to its simplicity. Several modifications have been considered to improve the adaptation performance based on the spectral properties of the driving signal including orthogonalisation procedures like the recursive LMS, the fast affine projection or frequency domain methods. These methods are extremely sensitive to observation noise. A new finite correlation LMS (FCLS) algorithm is proposed which yields increased noise robustness in noise contaminated environments. Under the set of conditions considered an adaptation improvement of about 23 dB is obtained by replacing NLMS by FCLS. Further improvements can be obtained if chaotic coding is used.

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Section: B Adaptation Using Chaotic Coded Speechsupporting

confidence: 59%