2006 |
Dimitrios Dimitriadis, Petros Maragos Continuous energy demodulation methods and application to speech analysis Journal Article Speech Communication, 48 (7), pp. 819–837, 2006, ISSN: 01676393. Abstract | BibTeX | Links: [PDF] @article{121, title = {Continuous energy demodulation methods and application to speech analysis}, author = {Dimitrios Dimitriadis and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/DimitriadisMaragos_ContEnergDemodMethodApplicSpeechAnalysis_SpeCom2006.pdf}, doi = {10.1016/j.specom.2005.08.007}, issn = {01676393}, year = {2006}, date = {2006-01-01}, journal = {Speech Communication}, volume = {48}, number = {7}, pages = {819--837}, abstract = {Speech resonance signals appear to contain significant amplitude and frequency modulations. An efficient demodulation approach is based on energy operators. In this paper, we develop two new robust methods for energy-based speech demodulation and compare their performance on both test and actual speech signals. The first method uses smoothing splines for discrete-to-continuous signal approximation. The second (and best) method uses time-derivatives of Gabor filters. Further, we apply the best demodulation method to explore the statistical distribution of speech modulation features and study their properties regarding applications of speech classification and recognition. Finally, we present some preliminary recognition results and underline their improvements when compared to the corresponding MFCC results. ?? 2005 Elsevier B.V. All rights reserved.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Speech resonance signals appear to contain significant amplitude and frequency modulations. An efficient demodulation approach is based on energy operators. In this paper, we develop two new robust methods for energy-based speech demodulation and compare their performance on both test and actual speech signals. The first method uses smoothing splines for discrete-to-continuous signal approximation. The second (and best) method uses time-derivatives of Gabor filters. Further, we apply the best demodulation method to explore the statistical distribution of speech modulation features and study their properties regarding applications of speech classification and recognition. Finally, we present some preliminary recognition results and underline their improvements when compared to the corresponding MFCC results. ?? 2005 Elsevier B.V. All rights reserved. |
1997 |
B. Santhanam, P. Maragos Demodulation of discrete multicomponent AM-FM signals using periodic algebraic separation and energy demodulation Conference 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 3 , 1997, ISSN: 1520-6149. Abstract | BibTeX | Links: [PDF] @conference{271, title = {Demodulation of discrete multicomponent AM-FM signals using periodic algebraic separation and energy demodulation}, author = { B. Santhanam and P. Maragos}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=599542}, doi = {10.1109/ICASSP.1997.599542}, issn = {1520-6149}, year = {1997}, date = {1997-01-01}, booktitle = {1997 IEEE International Conference on Acoustics, Speech, and Signal Processing}, volume = {3}, pages = {2409--2412}, abstract = {Existing multicomponent AM-FM demodulation algorithms either assume spectrally distinct components or components separable via linear filtering and break down when the components overlap spectrally or if one of the components is stronger than the other. In this paper, we present a nonlinear algorithm for multicomponent AM-FM demodulation which avoids the above shortcomings and works well even for extremely small spectral separation of the components. The proposed algorithm separates the multicomponent demodulation problem into two tasks: periodicity-based algebraic separation of the components and then monocomponent demodulation via energy-based methods}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Existing multicomponent AM-FM demodulation algorithms either assume spectrally distinct components or components separable via linear filtering and break down when the components overlap spectrally or if one of the components is stronger than the other. In this paper, we present a nonlinear algorithm for multicomponent AM-FM demodulation which avoids the above shortcomings and works well even for extremely small spectral separation of the components. The proposed algorithm separates the multicomponent demodulation problem into two tasks: periodicity-based algebraic separation of the components and then monocomponent demodulation via energy-based methods |
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