Audio noise suppression based on neuromorphic saliency and phoneme adaptive filtering

2004 IEEE 11th Digital Signal Processing Workshop and 2nd IEEE Signal Processing Education Workshop

Volumn , Issue , 2004, Pages 361-365

  Hu, Rongqiang ^a   Anderson, David V ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FILTERING; ALGORITHMS; APPROXIMATION THEORY; BANDWIDTH; KALMAN FILTERING; PARAMETER ESTIMATION; SIGNAL DETECTION; SIGNAL TO NOISE RATIO; SPEECH;

NOISE SUPPRESSION; NOISE TRACKING; SPEECH ENHANCEMENT; SPEECH SEGMENTATION;

ACOUSTIC NOISE;

EID: 17044400957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Y. Ephraim and D. Malah Speech enhancement suing optimal non-linear spectral amplitude estimation, the International Conference on Acoustics, Speech, and Signal Processing-ICASSP 1983, Boston, USA
2. W. Kim, S. Kang, and H. Ko, Spectral substraction based on phonetic dependency and masking effects, IEE Pro.-Vis. Image Process., Vol. 147, No. 5, October 2000.
3. S.F. Boll Suppresion of acoustic noise in speech using spectral subtrction, IEEE Trans. Acoust. Speech Signal Prcoess, 1979, ASSP-27, (2), pp.113-120.
4. M.E. Deidher, and A.S. Spanias HMM-based speech enhancement using harmonic modelling, Proceedings of IEEE ICASSF97, Munich, Germany, 1997, pp.1175-1179.
5. P.S. Whitehead, D.V.Anderson and M.A. Clements, Adaptive, acoustic noise suppression for speech enhancement, Multimedia and Expo, 2003. ICME '03. Proceedings. 2003 International Conference on, Volume: 1, 6-9 July 2003
6. P. Vary, Noise suppression by spectral magnitude estimation-mechanism and theoretical limits, Signal Process., vol. 8, pp. 387400, 1985.
7. Y. Ephraim and H. L. V. Trees, A signal subspace approach for speech enhancement, IEEE Trans. Speech Audio Processing, vol. 3, pp. 251266, 1995.
8. W. Yang, M. Benbouchta, and R. Yantorno, Performance of the modified bark spectral distortion as an objective speech quality measure, in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 1998, pp. 541544.
9. S.Wang, A. Sekey, and A. Gersho, An objective measure for predicting subjective quality of speech coders, IEEE J. Select. Areas Commun., vol. 10, pp. 819829, 1992.
10. J.W. Picone, Signal modeling techniques in speech recognition, Proceedings of the IEEE, Volume: 81 Issue: 9, pp.1215-1247, Sept. 1993
11. D.R. Edgington, D. Walther, D.E. Cline, R. Sherlock and C. Koch, Detecting and tracking animals in underwater video using a neuromorhpic saliency-based attention system, 2004 Ocean Research Conference February 15-20, 2004, Honolulu, Hawaii, USA