Analysis on Quranic Accents Automatic Identification with Acoustic Echo Cancellation using Affine Projection and Probabilistic Principal Component Analysis

Background/Objectives: Audio recordings or live recitation is easy for noise and reverberation prone and contributes to the classification results. Methods/Statistical Analysis: Therefore, this paper proposed suitable usage of Affine Projection (AP) as adaptive filtering can be used to remove the echo and improve the percentage of results after the classification method takes place. Findings: The room impulse response is added for the signals in the study and AP algorithm is applies to remove the echo existing on each signal. Later, 161 signals then are converted to feature vector by Mel Frequency Cepstral Coefficients (MFCC) and the classification method using Probabilistic Principal Component Analysis (PPCA). This finding is to find the accuracies results based on audio on Quranic accents recitations after acoustic echo cancellation is done on the echoed signal. Applications/Improvements: The percentage of accuracies increases when acoustic echo cancellation is in used from the overall classification processes done to the segmented verses of the Quranic verse for Surah Ad-Duhaa. The implementation of AP as adaptive algorithm able to reduce the echo of Quranic accents and consistent output on the pattern classification and with 20dB of room impulse reverberation is applied and finally it can improve the results of classification to 96%.