Noise Cancellation Frontends For Automatic Meeting ... - CiteSeerX

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Noise Cancellation Frontends For Automatic Meeting ... - CiteSeerX

Euronoise 2006, Tampere, Finland

Képesi, Pham, Kubin, Weruaga, Juffinger, Grabner

5.3 Angle estimation performance

Noise cancellation methods were also evaluated from a viewpoint of handling the complex

characteristics of meeting noise for DoA estimation. The results are depicted on Fig.1 (right).

The spectral subtraction (SS) method enhanced the DoA performance by 1% (20dB) and 3%

(10dB) in comparison to unprocessed data, while the WPD method rises the performance by

2% (20dB) and 14% (10dB).

6 CONCLUSIONS

Two different noise suppression methods have been evaluated for a noise-robust speaker

localization algorithm as part of the Mistral automatic meeting transcription system. The

methods have been compared by applying three various measures: precision, recall and

effectiveness. As the evaluation has shown, the DoA performance strongly depends on the

VAD preciseness. Good precision of DoA is achieved only at low VAD insertions. The

Statistical Wavelet Filtration method with flexible multi-resolution analysis outperforms

Spectral Subtraction in both tasks: Voice Activity Detection and Direction of Arrival. Our

future work includes the investigation of harmonicity-based VAD and an enhanced SS

method based on the Fan-Chirp Transform. Furthermore, the integration of a video-based

speaker tracking algorithm is planned.

ACKNOWLEDGEMENTS

The project results have been developed in the MISTRAL Project, financed by the Austrian

Research Promotion Agency (www.ffg.at) within the strategic objective FIT-IT under the

project contract number 809264/9338.

REFERENCES

[1] Philips SpeechMagic webpage, www.speech.philips.com , 2006.

[2] AMI project webpage, www.amiproject.org, 2006.

[3] The webpage of Aurora group, ETSI ES 202 211 (2003-11).

[4] S. Boll, “Suppression of acoustic noise in speech using spectral subtraction”, IEEE

Trans. Acoust., Speech, Signal Processing, Vol. 27, pp. 113-120, Apr. 1979.

[5] V. Stahl, A. Fischer, and R. Bippus, “Quantile based noise estimation for spectral

subtraction and wiener filtering", Proc. ICASSP, Vol. 3, pp. 1875-1878, Istanbul,

Turkey, 2000.

[6] T. V. Pham, Erhard Rank, Gernot Kubin, “Noise Suppression Based On Wavelet Packet

Decomposition and Quantile Noise Estimation For Robust Automatic Speech

Recognition”, ICASSP Conf., Toulouse, France, 14-19, May 2006.

[7] The webpage of the Mistral Project, www.mistral-project.at

[8] Max Planck Institute for Psycholinguistics, MPI-EL Tools, www.mpi.nl/tools/elan.html.

[9] M. Grabner, H. Grabner and H. Bischof, “Fast Approximated SIFT”, Proc. Asian

Conference on Computer Vision, 2006

[10] C.J. Van Rijsbergen, “Information Retrieval, 2nd edition”, Butterworth-Heinemann

Newton, 1979

6

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