Time-frequency analysis based methods for classification of newborn cry signals/
Since, the t-f analysis is a good approach for analyzing the highly non-stationary characteristic, in time and frequency scale simultaneously without eliminating any salient information, this research work address the development of an objective method for classifying different infant cry signals pr...
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| Format: | Thesis Book |
| Language: | English |
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Perlis, Malaysia
School of Mechatronic Engineering
2016
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| 100 | 1 | |a Jeyaraman, Saraswathy, |e author | |
| 245 | 1 | 0 | |a Time-frequency analysis based methods for classification of newborn cry signals/ |c Saraswathy A/P Jeyaraman |
| 264 | 1 | |a Perlis, Malaysia |b School of Mechatronic Engineering |c 2016 | |
| 300 | |a xvi, 239 pages |b colour illustration |c 30 cm. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 502 | |a Thesis (Doctor of Philosophy) -- Universiti Malaysia Perlis. School of Mechatronic Engineering, 2016 | ||
| 504 | |a Includes bibliographical references. | ||
| 520 | |a Since, the t-f analysis is a good approach for analyzing the highly non-stationary characteristic, in time and frequency scale simultaneously without eliminating any salient information, this research work address the development of an objective method for classifying different infant cry signals predominantly using two different t-f methods namely QTFDs (spectrogram (SPEC), Wigner-Ville distribution (WVD), Smoothed-Wigner Ville distribution (SWVD), Choi-William distribution (CWD) and Modified B-distribution (MBD)) and WPT based method (wavelet packet spectrum (Wpspectrum)). A cluster of t-f based features was extracted from the suggested t-f methods and their efficacy was examined using two supervised neural networks, namely probabilistic neural network (PNN) and general regression neural network (GRNN). | ||
| 541 | |a Gift from Centre for Graduate Studies (Doctor of Philosophy) |b 2017 | ||
| 650 | 0 | |a Crying in infants | |
| 650 | 0 | |a Signal processing | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Wavelets (Mathematics) | |
| 650 | 0 | |a Wigner distribution | |
| 710 | 2 | |a Universiti Malaysia Perlis | |
| 720 | 1 | |a Dr. M. Hariharan, |e supervisor | |
| 790 | 1 | |a School of Mechatronic Engineering | |
| 791 | 1 | |a Doctor of Philosophy | |
| 792 | 1 | |a 2016 | |
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