Non-Dyadic Wavelet Analysis

Pollock, Stephen M.; Cascio, Iolanda Lo

doi:10.1007/3-540-36626-1_9

Cited by 14 publications

(7 citation statements)

References 15 publications

(15 reference statements)

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“…The WD process involves the splitting of the signal into the low and high‐frequency components named approximate (A) and detail (D), from which the approximate and detail coefficients are formed [38–40].…”

Section: Methodsmentioning

confidence: 99%

Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

Nsugbe

Samuel

Asogbon

et al. 2021

CAAI Trans on Intel Tech

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In signal processing, multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels, where the optimal level within the signal minimises redundancy, uncertainties, and contains the information required for the characterisation of the sensed phenomena. In the area of physiological signal processing for prosthesis control, scenarios where a signal decomposition analysis are required: the wavelet decomposition (WD) has been seen to be the favoured time-frequency approach for the decomposition of non-stationary signals. From a research perspective, the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification. Despite this, there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity. Here, for neuromuscular (electromyography) and brainwave (electroencephalography) signals acquired from a transhumeral amputee, a computationally efficient time domain signal decomposition method based on a series of heuristics was applied to process the acquired signals before feature extraction. The results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

Nsugbe

Samuel

Asogbon

et al. 2021

CAAI Trans on Intel Tech

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“…The major benefit of using wavelet transform is that, unlike using single fixed sized analysis window in STFT, it uses windows with variable duration. The high frequency portion of the speech signal is processed by the short duration window, whereas the low frequency part of the speech signal is processed by the long duration window [24,[26][27]. Thus by applying wavelet transform to a speech signal, it can be inspected for the presense or absence of sudden burst (stop phonemes) in a slowly changing signal [20,22].…”

Section: Related Workmentioning

confidence: 99%

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Mahadevaswamy

Ravì

2021

Wireless Pers Commun

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An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 75% of collected speech data is used for training the acoustic models and 25% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, theproposed features shows an improvement in WRA of 1.79% over baseline features.

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“…The equality follows from the identity sin(A + B) − sin(A − B) = 2 cos A sin B. It has been shown by Pollock and Lo Cascio (2006) that, when the interval [0, π] is partitioned by a sequence of p frequency bands of equal width, an orthogonal basis can be obtained for each band by displacing its wavelets successively by p elements at a time. The implication is that, to obtain full information on a process that is limited to such a band, we need only sample it at the rate of one observation in p sample periods.…”

Section: The Processes Underlying the Datamentioning

confidence: 99%

Statistical Signal Extraction and Filtering: A Partial Survey

Pollock

2009

Handbook of Computational Econometrics

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A wide variety of techniques have been devised for the purpose of extracting the components of econometric data sequences. The traditional approach, which continues to prevail in the central statistical agencies of many countries, has relied upon filters that have been derived with refernce to the principles of actuarial graduation. Latterly, filters that are grounded in the Wiener-Kolmogorov theory of signal extraction have become prominent in academic research. There are several alternative ways of applying the Wiener-Kolmogorov filters to the data.

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Non-Dyadic Wavelet Analysis

Cited by 14 publications

References 15 publications

Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Statistical Signal Extraction and Filtering: A Partial Survey

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