Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Processing Nonlinearities Read chapter Read first chapter

2019 | OriginalPaper | Chapter

4. Neural Beamforming for Speech Enhancement: Preliminary Results

Authors : Stefano Tomassetti, Leonardo Gabrielli, Emanuele Principi, Daniele Ferretti, Stefano Squartini

Published in: Neural Advances in Processing Nonlinear Dynamic Signals

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In the field of multi-channel speech quality enhancement, beamforming algorithms play a key role, being able to reduce noise and reverberation by spatial filtering. To that extent, an accurate knowledge of the Direction of Arrival (DOA) is crucial for the beamforming to be effective. This paper reports extremely improved DOA estimates with the use of a recently introduced neural DOA estimation technique, when compared to a reference algorithm such as Multiple Signal Classification (MUSIC). These findings motivated for the evaluation of beamforming with neural DOA estimation in the field of speech enhancement. By using the neural DOA estimation in conjunction with beamforming, speech signals affected by reverberation and noise improve their quality. These first findings are reported to be taken as a reference for further works related to beamforming for speech enhancement.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Data Mining by Evolving Agents for Clusters Discovery and Metric Learning
next chapter Error Resilient Neural Networks on Low-Dimensional Manifolds
Footnotes
Literature
1.
Allen, J., Berkley, D.: Image method for efficiently simulating small-room acoustics. J. Acoust. Soc. Am., 943 (1979)CrossRef
2.
Araki, S., Hayashi, T., Delcroix, M., Fujimoto, M., Takeda, K., Nakatani, T.: Using neural network front-ends on far field multiple microphones based speech recognition. In: Proceedings of ICASSP, Florence, Italy, pp. 5542–5546, 4–9 May 2014
3.
Araki, S., Hayashi, T., Delcroix, M., Fujimoto, M., Takeda, K., Nakatani, T.: Exploring multi-channel features for denoising-autoencoder-based speech enhancement. In: Proceedings of ICASSP, pp. 116–120 (2015)
4.
Benesty, J., Chen, J., Huang, Y.: Microphone Array Signal Processing, vol. 1. Springer Science & Business Media (2008)
5.
Capon, J.: High resolution frequency-wavenumber spectrum analysis. Proc. IEEE 57(8), 1408–1418 (1969)CrossRef
6.
Ephraim, Y., Malah, D.: Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. Acoust. Speech Signal Process. 32(6), 1109–1121 (1984)CrossRef
7.
Erdogan, H., Hayashi, T., Hershey, J.R., Hori, T., Hori, C., Hsu, W.n., Kim, S., Roux, J.L., Meng, Z., Watanabe, S.: Multi-channel speech recognition: LSTMs all the way through. In: Proceedings of the 4th CHiME Speech Separation and Recognition Challenge, San Francisco, CA, USA (2016)
8.
Gannot, S., Cohen, I.: Speech enhancement based on the general transfer function gsc and postfiltering. IEEE Trans. Speech Audio Process. 12(6), 561–571 (2004)CrossRef
9.
Griffiths, L., Jim, C.: An alternative approach to linearly constrained adaptive beamforming. IEEE Trans. Antennas Propag. 30(1), 27–34 (1982)CrossRef
10.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural comput. 9(8), 1735–1780 (1997)CrossRef
11.
Hoshen, Y., Weiss, R., Wilson, K.: Speech Acoustic Modeling from Raw Multichannel Waveforms, pp. 4624–4628 (2015)
12.
Hussain, A., Chetouani, M., Squartini, S., Bastari, A., Piazza, F.: Nonlinear Speech Enhancement: An Overview, pp. 217–248. Springer Berlin (2007)
13.
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)
14.
Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Sehr, A., Kellermann, W., Maas, R.: The REVERB challenge: a common evaluation framework for dereverberation and recognition of reverberant speech. In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 1–4. IEEE (2013)
15.
Knapp, C., Carter, G.: The generalized correlation method for estimation of time delay. IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327 (1976)CrossRef
16.
Knecht, W., Schenkel, M.E., Moschytz, G.S.: Neural network filters for speech enhancement. IEEE Trans. Speech Audio Process. 3(6), 433–438 (1995)CrossRef
17.
Li, B., Sainath, T., Weiss, R., Wilson, K., Bacchiani, M.: Neural network adaptive beamforming for robust multichannel speech recognition. In: Proceedings of Interspeech, pp. 1976–1980, 8–12 Sept 2016
18.
Li, J., Deng, L., Haeb-Umbach, R., Gong, Y.: Robust Automatic Speech Recognition: A Bridge to Practical Applications. Academic Press (2015)
19.
Loizou, P.: Speech processing in vocoder-centric cochlear implants. In: Cochlear and Brainstem Implants, vol. 64, pp. 109–143. Karger Publishers (2006)CrossRef
20.
Philipos C. Loizou: Speech Enhancement: Theory and Practice. CRC Press (2013)
21.
Principi, E., Fuselli, D., Squartini, S., Bonifazi, M., Piazza, F.: A speech-based system for in-home emergency detection and remote assistance. In: Proceedings of the 134th International AES Convention, Rome, Italy, pp. 560–569, 4–7 May 2013
22.
Principi, E., Squartini, S., Bonfigli, R., Ferroni, G., Piazza, F.: An integrated system for voice command recognition and emergency detection based on audio signals. Expert Syst. Appl. 42(13), 5668–5683 (2015)CrossRef
23.
Principi, E., Squartini, S., Piazza, F.: Power normalized cepstral coefficients based supervectors and i-vectors for small vocabulary speech recognition. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Beijing, China, pp. 3562–3568, 6–11 July 2014
24.
Renals, S., Swietojanski, P.: Neural networks for distant speech recognition. In: Proceedings of HSCMA, pp. 172–176 (2014)
25.
Robinson, T., Fransen, J., Pye, D., Foote, J., Renals, S.: WSJ-CAM0: a british english corpus for large vocabulary continuous speech recognition. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing (1994)
26.
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)CrossRef
27.
Sohn, J., Kim, N.S., Sung, W.: A statistical model-based voice activity detection. IEEE Signal Process. Lett. 6(1), 1–3 (1999)CrossRef
28.
Swietojanski, P., Ghoshal, A., Renals, S.: Convolutional neural networks for distant speech recognition. IEEE Signal Process. Lett. 21(9), 1120–1124 (2014)CrossRef
29.
Xiao, X., Watanabe, S., Erdogan, H., Lu, L., Hershey, J., Seltzer, M., Chen, G., Zhang, Y., Mandel, M., Yu, D.: Deep beamforming networks for multi-channel speech recognition. In: Proceedings of ICASSP, pp. 5745–5749 (2016)
30.
Xiao, X., Zhao, S., Zhong, X., Jones, D.L., Chng, E.S., Li, H.: A learning-based approach to direction of arrival estimation in noisy and reverberant environments. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2814–2818. IEEE (2015)
31.
Yoganathan, V., Moir, T.: Multi-microphone adaptive neural switched Griffiths-Jim beamformer for noise reduction. In: Proceedings of the 10th International Conference on Signal Processing, pp. 299–302 (2010)
32.
Zhang, H., Zhang, X., Gao, G.: Multi-channel speech enhancement based on deep stacking network. In: Proceedings of the 4th CHiME Speech Separation and Recognition Challenge, San Francisco, CA, USA (2016)
Metadata
Title
Neural Beamforming for Speech Enhancement: Preliminary Results
Authors
Stefano Tomassetti
Leonardo Gabrielli
Emanuele Principi
Daniele Ferretti
Stefano Squartini
Copyright Year
2019
Book
Neural Advances in Processing Nonlinear Dynamic Signals

Print ISBN: 978-3-319-95097-6

Electronic ISBN: 978-3-319-95098-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-95098-3_4

Premium Partner

    Image Credits