The Hartley Phase Spectrum as an Assistive Feature for Classification

Paraskevas, Ioannis; Rangoussi, Maria

doi:10.1007/978-3-642-11509-7_7

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Moreover, the same rates are 36.3% and 39.1% higher for the four "hard" classes in Experiments 1 and 2 in Table 3, respectively, as compared with the use of the MFCCs only. It is important to mention that similar conclusions are drawn from the classification results obtained from speech rather than audio signals [17]. It is also important that the higher classification rates obtained when phase spectral features are combined with magnitude spectral features have been observed uniformly across all the experiments carried out here.…”

Section: Discussionsupporting

confidence: 74%

Feature Extraction for Audio Classification of Gunshots Using the Hartley Transform

Paraskevas¹,

Rangoussi²

2012

OJA

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In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function; thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used

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Section: Discussionsupporting

confidence: 74%

Feature Extraction for Audio Classification of Gunshots Using the Hartley Transform

Paraskevas¹,

Rangoussi²

2012

OJA

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“…It plays a wide role to recognize the pattern [13][14][15]. 'Hartley Phase Spectrum (HPS)' summarizes the phase content of the function more accurately, compared with its Fourier counter audio (gunshot) classification [16], speech (phoneme) classification [17] Revised…”

Section: Introductionmentioning

confidence: 99%

Development of Quantum Hartley Transform for Signal Processing Applications

jain*,

Jain,

Jain

2020

IJITEE

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The present paper deals with the q-analogue of Hartley transform which is the q-extension of Hartley transform. It is an orthogonal transform which is defined in the domain -∞ to ∞ whose kernel is (cosinusidal) function ‘cas’ which is a combination of trigonometrical functions ‘sin’ and ‘cos’. The q-analogue of Hartley transform is defined in the domain -∞ to ∞ whose kernel is ‘cas_q’ function which is a q-extension of ‘cas’ function. In the similar manner ‘cas_q’ is a q-extension of combination of q-extension of trigonometrical functions ‘sin_q’ and ‘cos_q’ defined in Kack and Chengue book [18]. In this paper we will establish some basic properties of q-Hartley transform, for instance linear property, change of scale property.

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“…Thus, the ‘Whitened Hartley spectrum’ or ‘Hartley Phase Spectrum (HPS)’ encapsulates the phase content of the signal more efficiently, compared with its Fourier counterpart. Moreover, the HPS has already found useful application in: audio (gunshot) classification [19, 22], speech (phoneme) classification [23–25] and as a noise robust feature for signal analysis [26]. Please note that the terms ‘Whitened Hartley spectrum’ and ‘HPS’ are used in this paper interchangeably since both of them convey the same meaning.…”

Section: Introductionmentioning

confidence: 99%