A comparison of the squared energy and teager-kaiser operators for short-term energy estimation in additive noise

Dimitriadis, D; Potamianos, A; Maragos, P

dc.contributor.author	Dimitriadis, D	en
dc.contributor.author	Potamianos, A	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:29:32Z
dc.date.available	2014-03-01T01:29:32Z
dc.date.issued	2009	en
dc.identifier.issn	1053-587X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19288
dc.subject	Bandlimited signals	en
dc.subject	Estimation	en
dc.subject	Feature extraction	en
dc.subject	Harmonic analysis	en
dc.subject	Noise	en
dc.subject	Robustness	en
dc.subject	Signal detection	en
dc.subject	Spectral analysis	en
dc.subject	Time-frequency analysis	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Bandlimited signals	en
dc.subject.other	Noise	en
dc.subject.other	Robustness	en
dc.subject.other	Spectral analysis	en
dc.subject.other	Time-frequency analysis	en
dc.subject.other	Additive noise	en
dc.subject.other	Error analysis	en
dc.subject.other	Estimation	en
dc.subject.other	Feature extraction	en
dc.subject.other	Fourier series	en
dc.subject.other	Harmonic analysis	en
dc.subject.other	Signal detection	en
dc.subject.other	Signal processing	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Spectrum analyzers	en
dc.subject.other	Speech processing	en
dc.subject.other	Windows	en
dc.subject.other	Frequency estimation	en
dc.title	A comparison of the squared energy and teager-kaiser operators for short-term energy estimation in additive noise	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TSP.2009.2019299	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TSP.2009.2019299	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Time-frequency distributions that evaluate the signal's energy content both in the time and frequency domains are indispensable signal processing tools, especially, for nonstationary signals. Various short-time energy computation schemes are used in practice, including the mean squared amplitude and Teager-Kaiser energy approaches. Herein, we focus primarily on the short- and medium-term properties of these two energy estimation schemes, as well as, on their performance in the presence of additive noise. To facilitate this analysis and generalize the approach, we use a harmonic noise model to approximate the noise component. The error analysis is conducted both in the continuous- and discrete-time domains, deriving similar conclusions. The estimation errors are measured in terms of normalized deviations from the expected signal energy and are shown to greatly depend on both the signals' spectral content and the analysis window length. When medium- and long-term analysis windows are employed, the Teager-Kaiser energy operator is proven superior to the common squared energy operator, provided that the spectral content of the noise is more lowpass than the corresponding signal content, and vice versa. However, for shorter window lengths, the Teager-Kaiser operator always outperforms the squared energy operator. The theoretical results are experimentally verified for synthetic signals. Finally, the performance of the proposed energy operators is evaluated for short-term analysis of noisy speech signals and the implications for speech processing applications are outlined. © 2009 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Signal Processing	en
dc.identifier.doi	10.1109/TSP.2009.2019299	en
dc.identifier.isi	ISI:000267379200013	en
dc.identifier.volume	57	en
dc.identifier.issue	7	en
dc.identifier.spage	2569	en
dc.identifier.epage	2581	en