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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Salina Abdul Samad
Universiti Kebangsaan Malaysia
Electrical & Electronics Engineering

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Objective and Subjective Evaluations of Adaptive Noise Cancellation Systems with Selectable Algorithms for Speech Intelligibility Roshahliza M. Ramil; Salina Abdul Samad; Ali O. Abid Noor
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (953.913 KB) | DOI: 10.11591/eei.v7i4.1183

Abstract

Adaptive Noise Cancellation (ANC) systems with selectable algorithms refer to ANC systems that are able to change the adaptation algorithm based on the eigenvalue spread of the noise. These systems can have dual inputs based on the conventional ANC structure or a single input based on the Adaptive Line Enhancer (ALE) structure. This paper presents a comparison of the performance of these two systems using objective and subjective measurements for speech intelligibility. The parameters used to objectively compare the systems are the Mean Square Error (MSE) and the output Signal to Noise Ratio (SNR). For subjective evaluation, listening tests were evaluated using the Mean Opinion Score (MOS) technique. The outcomes demonstrate that for both objective and subjection evaluations, the single input ALE with selectable algorithms (S-ALE) system outperforms that of the dual input ANC with selectable algorithm (S-ANC) in terms of better steady-state MSE by 10%, higher SNR values for most types of noise, higher scores in most of the questions in the MOS questionnaire and a higher acceptance rate for speech quality.
Improving spectrogram correlation filters with time-frequency reassignment for bio-acoustic signal classification Salina Abdul Samad; Aqilah Baseri Huddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp59-64

Abstract

Spectrogram features have been used to automatically classify animals based on their vocalization. Usually, features are extracted and used as inputs to classifiers to distinguish between species. In this paper, a classifier based on Correlation Filters (CFs) is employed where the input features are the spectrogram image themselves.  Spectrogram parameters are carefully selected based on the target dataset in order to obtain clear distinguishing images termed as call-prints. An even better representation of the call-prints is obtained using spectrogram Time-Frequency (TF) reassignment. To demonstrate the application of the proposed technique, two species of frogs are classified based on their vocalization spectrograms where for each species a correlation filter template is constructed from multiple call-prints using the Maximum Margin Correlation Filter (MMCF). The improved accuracy rate obtained with TF reassignment demonstrates that this is a viable method for bio-acoustic signal classification.
Co-Authors Ali O. Abid Noor Aqilah Baseri Huddin Roshahliza M. Ramil