Nurul Wahidah, Arshad and S. N., Abdul Aziz and Faradila, Naim and Rohana, Abdul Karim and Rosyati, Hamid and Nor Farizan, Zakaria (2011) Speech processing for makhraj recognition: The design of adaptive filter for noise canceller. In: IEEE 7th International Conference on Information Technology in Asia , 12-13 July 2011 , Kuching, Sarawak. pp. 1-9.. ISBN 978-1-61284-130-4
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Abstract
In our daily life, improvement of makhraj for Arabic alphabets is a topic that very useful in many applications and environments. The existing systems cannot recognize the appropriate pronunciation of each alphabet with the existence of noise. As an example of “ha”, the system may recognize wrong alphabet like “kho”. This paper focuses on noise removal in makhraj recognition using Normalized Least Mean Square (NLMS) Algorithm based on Adaptive Filter to search for the optimal solution. There are 30 Arabic alphabets from i until s. However, this project will only use 7 alphabets as samples, they are i to c. The speech processing is used to obtain same waveform output from two different situations. The filtered data is processed to match the standard pronunciations and it is integrated with filter design process in MATLAB. From the result, the waveform of noise cancellation using NLMS algorithm is quite similar with the waveform of reference signal. It is proved that noise cancellation method remove noise from unknown system.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Adaptive filter; Least Mean Square (LMS) algorithm; Normalized Least Mean Square (NLMS) algorithm; System Identification; Noise Cancellation |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 29 Nov 2019 07:30 |
| Last Modified: | 29 Nov 2019 07:30 |
| URI: | http://umpir.ump.edu.my/id/eprint/25945 |
| Download Statistic: | View Download Statistics |
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