Sub-band SNR estimation using auditory feature processing

Speech Communication

Volumn 39, Issue 1-2, 2003, Pages 47-63

  Kleinschmidt, Michael ^a   Hohmann, Volker ^a  

^a UNIVERSITY OF OLDENBURG   (Germany)

Author keywords

Auditory front end; Neural networks; Sigma pi cells; Situation classification; Sub band SNR estimation

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; AUDITION; ERROR ANALYSIS; HEARING AIDS; MODULATION; NEURAL NETWORKS; SIGNAL TO NOISE RATIO; SPEECH RECOGNITION;

AUTOMATIC SPEECH RECOGNITION (ASR);

ACOUSTICS;

EID: 0037211087     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-6393(02)00058-4     Document Type: Article

References (32)

1. Allen J.B. How do humans process and recognize speech. IEEE Trans. Speech Audio Process. 2(4):1994;567-576.
2. Avendano C., Hermansky H., Vis M., Bayya A. Adaptive speech enhancement using frequency-specific SNR estimates. Proc. IEEE IVTTA'96, Basking Ridge, N.J. 1996;65-68.
3. Bourlard H., Dupont S., Hermansky H., Morgan N. Towards sub-band-based speech recognition. European Signal Proc. Conf., Trieste. 1996;1579-1582.
4. Chi T., Gao Y., Guyton M.C., Ru P., Shamma S. Spectro-temporal modulation transfer functions and speech intelligibility. J. Acoust. Soc. Amer. 106(5):1999;2719-2732.
5. Dau T., Püschel D., Kohlrausch A. A quantitative model of the "effective" signal processing in the auditory system: I. Model structure. J. Acoust. Soc. Amer. 99:1996;3615-3622.
6. Dau T., Kollmeier B., Kohlrausch A. Modeling auditory processing of amplitude modulation: I. Modulation detection and masking with narrowband carriers. J. Acoust. Soc. Amer. 102(2):1997;2892-2905.
7. deCharms R.C., Blake D.T., Merzenich M.M. Optimizing sound features for cortical neurons. Science. 280:1998;1439-1443.
8. Dupont S., Ris C. Assessing local noise level estimation methods. Proc. Workshop on Robust Methods for Speech Recognition in Adverse Environments, Tampere, Finland. 1999;115-118.
9. Gramß T. Fast algorithms to find invariant features for a word recognizing neural net. IEEE 2nd Internat. Conf. on Artificial Neural Networks, Bournemouth. 1991;180-184.
10. Gramß T., Strube H.W. Recognition of isolated words based on psychoacoustics and neurobiology. Speech Communication. 9:1990;35-40.
11. Hagan M.T., Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks. 5(6):1994;989-993.
12. Hansen M., Kollmeier B. Objective modeling of speech quality with a psychoacoustically validated auditory model. J. Audio Eng. Soc. 48(5):2000;395-409.
13. Hermansky H., Morgan N. RASTA processing of speech. IEEE Trans. Speech Audio Process. 2(4):1994;578-589.
14. Hermansky H., Sharma S. TRAPS - Classifiers of temporal patterns. Proc. ICSLP'98. Vol. 3:1998;1003-1006.
15. Hirsch, H.G., 1993. Estimation of noise spectrum and its applications to SNR-estimation and speech enhancement. Technical Report TR-93-012, International Computer Science Institute, Berkeley, California, USA.
16. Hirsch H.G., Ehrlicher C. Noise estimation techniques for robust speech recognition. Proc. Internat. Conf. on Acoust., Speech and Signal Process. (ICASSP). 1995;153-156 IEEE.
17. Hohmann, V., 2002. Frequency analysis and synthesis using a gammatone filterbank. Acta Acustica united with Acustica, no. 3, May/June, pp. 433-442.
18. Kaernbach, C., 2000. Early auditory feature coding. In: Contributions to psychological acoustics: Results of the 8th Oldenburg Symposium on Psychological Acoustics. BIS, Universität Oldenburg, pp. 295-307.
19. Kingsbury B., Morgan N., Greenberg S. Robust speech recognition using the modulation spectrogram. Speech Communication. 25(1):1998;117-132.
20. Kleinschmidt M., Hohmann V. Perzeptive Vorverarbeitung und automatische Selektion sekundärer Merkmale zur robusten Spracherkennung. Fortschritte der Akustik - DAGA 2000. 2000;382-383 DEGA, Oldenburg.
21. Kleinschmidt M., Tchorz J., Kollmeier B. Combining speech enhancement and auditory feature extraction for robust speech recognition. Speech Communication. 34:2001;75-91. (special issue on Robust ASR).
22. Kohler, K., Lex, G., Pätzold, M., Scheffers, M., Simpson, A., Thon, W., 1994. Handbuch zur Datenaufnahme und Transliteration in TP14 von VERBMOBIL-3.0. Tech. rep., Verbmobil-Technischer Report.
23. Kollmeier B., Koch R. Speech enhancement based on physiological and psychoacoustical models of modulation perception and binaural interaction. J. Acoust. Soc. Amer. 95(3):1994;1593-1602.
24. Martin R. An efficient algorithm to estimate the instantaneous SNR of speech signals. Proc. Eurospeech. 1993;1093-1096 ESCA.
25. Moore B.C.J., Glasberg B.R. Suggested formulae for calculating auditory-filter bandwidths and excitation patterns. J. Acoust. Soc. Amer. 74:1983;750-753.
26. Patterson, R.D., Nimmo-Smith, J., Holdsworth, J., Rice, P., 1987. An efficient auditory filterbank based on the gammatone function. Paper presented at a meeting of the IOC Speech Group on Auditory Modeling at RSRE.
27. Püschel, D., 1988. Prinzipien der zeitlichen Analyse beim Hören. Doctoral thesis, Universität Göttingen.
28. Ris C., Dupont S. Assessing local noise level estimation methods: application to noise robust ASR. Speech Communication. 34:2001;141-158.
29. Tchorz J., Kollmeier B. A model of the auditory perception as front end for automatic speech recognition. J. Acoust. Soc. Amer. 106(4):1999a;2040-2050.
30. Tchorz J., Kollmeier B. Speech detection and SNR prediction basing on amplitude modulation pattern recognition. Proc. Eurospeech. 1999b;2399-2404 ISCA, Budapest, Hungary.
31. Tchorz, J., Kollmeier, B., 2002. Estimation of the signal-to-noise ratio with amplitude modulation spectrograms. Speech Communication 38, 1-17.
32. Tchorz J., Kleinschmidt M., Kollmeier B. Noise suppression based on neurophysiologically-motivated SNR estimation for robust speech recognition. Leen T.K., Dietterich T.G., Tresp V. Advances in Neural Information Processing Systems 13 - NIPS 2000. 2001;821-827 MIT Press.