IMPLEMENTASI METODE ROTE LEARNING DALAM PEMBELAJARAN ILMU TAJWID

The present study aimed at finding out the portrayal of rote learning method implementation which was applied by the coaches of TPQ al-Barokah in Kampung Melayu Village, Rejang Lebong, Bengkulu. This study used a qualitative approach. The data were gathered by means of observation and interview techniques. After the data were collected, the data were analyzed using phases referring to Miles, Huberman, and Saldana comprising data condensation, data display, and drawing conclusion. The findings of this study concluded that the rote learning method for Tajwid learning was considered very effective to be applied, and its merit was evident in students’ competence in the recitation of Qur’an at TPQ al-Barokah. Their competence was seen when they were re-examined by their Qur’anic coaches, and they could provide examples of Tajwid’s application in the Qur-an properly and correctly.      

Efficient Music Fingerprinting for Music Speech Segregation

Since decade, a lot of music fingerprinting and speech segregation algorithms have exhaled. Music speech segregation includes music identification and followed by speech segregation. This becomes challenging in the presences of the noisy environment and noisy sample case. A rapid development has taken place in the field of multimedia content analysis. Music information retrieval applications increased the emphases on the development of music fingerprinting algorithms. Noise affects the efficiency and accuracy of the audio information retrieval algorithms. This research thesis presents a deep analysis of music fingerprinting and speech segregation algorithms. A novel algorithm is presented for music fingerprinting which is used for efficient speech segregation in which music fingerprinting is performed over a noisy audio sample. This research work proposes a system that performs music fingerprinting in-depth evolving the speech segregation processes in presence of background noise. Noise is removed from the audio signal using layered separation model of the recurrent neural network. Music fingerprinting is performed on the basis of pitch based acoustic features classified using distributed dictionary based features learning model. The classified music is processed for speech segregation after noise removal using layered separation model. Speech is segregated using vocal based acoustic features. Features are classified using improved dictionary based fisher algorithm. Structured based classes are used for the classification process. The systematic evaluation of the proposed system for music fingerprinting and speech segregation produces competitive results for three datasets (i.e. TIMIT, MIR-1K, and MusicBrainz), and the results indicate the strength of the proposed system. The proposed system produces significantly better results when the qualitative and quantitative analysis is carried out over the standard datasets showing the better efficiency of our proposed system from the past systems.