Abstract
The phase of a signal conveys critical information for feature extraction. In this work is shown that for certain speech and audio classes where their magnitude content underperforms in terms of recognition rate, the combination of magnitude with phase related features increases the classification rate compared to the case where only the magnitude content of the signal is used. However, signal phase extraction is not a straightforward process, mainly due to the discontinuities appearing in the phase spectrum. Hence, in the proposed method, the phase content of the signal is extracted via the Hartley Phase Spectrum where the sources of phase discontinuities are detected and overcome, resulting in a phase spectrum in which the number of discontinuities is significantly reduced.
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Paraskevas, I., Rangoussi, M. (2010). The Hartley Phase Spectrum as an Assistive Feature for Classification. In: Solé-Casals, J., Zaiats, V. (eds) Advances in Nonlinear Speech Processing. NOLISP 2009. Lecture Notes in Computer Science(), vol 5933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11509-7_7
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DOI: https://doi.org/10.1007/978-3-642-11509-7_7
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