Acoustic Echo Cancellation using Adaptive Filter for Quranic Accent Signals

Abstract

Quranic recordings and echoed portions of the emphasis are susceptible to signal reverberation, particularly when being listened to in a conference room. Tajweed and Quranic verse rule identification are susceptible to additive noise, which could lower classification accuracy. In order to reflect the most correct rate following pattern categorization, this study suggested the appropriate use of three adaptive algorithms: Affine Projection (AP), Least Mean Square (LMS), and Recursive Least Squares (RLS). For feature extraction, Mel Frequency Cepstral Coefficient is used together with Probabilities Principal Component Analysis (PPCA), K-Neural Network (KNN) and Gaussian Mixture Model (GMM). AP indicates 93.9% for all of the classification algorithm in used, while for LMS and RLS the results are differed varies on different pattern classification algorithm stated whereby with LMS and PPCA classification, 96.9 % for accuracy and 84.8% accuracy for LMS and KNN. While for RLS and GMM, 96.9% was achieved and the results were reduced for both KNN and PPCA. The analysis has  resulted for both on accuracies within different filtering algirithm and classification for accuracy and ERLE(dB).Towards this research it is hope will embark more understanding towards echo cancellation and quality of sound recordings that may affected even to the Quranic recordings.