Acoustic Echo Cancellation using Adaptive Filter for Quranic Accent Signals

Abstract

Quranic recordings andechoed 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.KeywordsAdaptive filtering, acoustic echo cancellation, recursive least squares, least mean square, affine projection, accuracy rate1.IntroductionUnusually big peaks have been produced by frequency response, masking, and emerging peaks from sound systems. Noise can be introduced into music recordings from the surrounding environment, during the recording system when audio signals change, or during the indexing process. These noise signals can disrupt and lower the quality and efficiency of the recording. Quranic recordings may also be affected by echoes, which can interfere with the recording process. The speaker, microphone, and transmission path are among the variables that affect.