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On Losses, Pauses, Jumps and the Wideband E-Model


A Low Complexity and High Effi

MICRO STRIP PATCH ANTENNA OPTI

Data Embedding in JPEG Bitstre
Abstract


There is an increasing interest in upgrading the EModel, a parametric tool for speech quality estimation, to the wideband and super-wideband contexts. The main motivation behind this has been to quantify the quality gain lent by various new codecs and communication situations. There have been numerous such contributions, and all of them have been more or less successful. This paper reports on an extension of the E-Model to the mixed narrowband/wideband (NB/WB) context. More specifically, we take a novel approach towards deriving effective equipment impairment factors (Ie; WB; eff ) by taking into account additional impairments related to the underlying communications network. These additional impairments are the pause and jump temporal discontinuities along with network-related loss and pure codec related impairments. While the effect of loss is a thoroughly studied topic and has been integrated into to the E-Model, pauses and jumps have been given little attention. Pauses and jumps manifest themselves as temporal dilation and contraction, respectively, in the resulting speech signal that is presented to the listener and are normally caused by jitter and jitter buffer interaction. In this work, we initially present a 4-state Markov model to characterise, and also emulate, loss, pause, and jump impairments. Then we present alternate models for computing effective equipment impairment models. A large number of test stimuli were generated using different NB and WB codecs. WBPESQ was used to evaluate the stimuli. Genetic programming (GP) was employed to derive equipment impairment factors. The proposed models have a high correlation with WB-PESQ. We claim that the models proposed by us outperform the existing E-Model by a factor of approximately 29Percent while using WBPESQ as a reference model. The models also outperform the EModel against results from auditory tests. It is also shown that the models outperform the results of multiple linear regression.

KeyWords
Loss, Pause, Jump, GP, WB-PESQ



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