International Journal of Speech Technology

, Volume 21, Issue 4, pp 851–859 | Cite as

DSP-based voice activity detection and background noise reduction

  • Charu SinghEmail author
  • Maarten Venter
  • Rajesh Kumar Muthu
  • David Brown


These days’ speech processing devices like voice-controlled devices, radio, and cell phones have gained more popularity in the area of military, audio forensics, speech recognition, education and health sectors. In the real world, speech signal during communication always contains background noise. The main task of speech related applications is voice activity detection (VAD) which include speech communication, speech recognition, and speech coding. Noise-reduction schemes for speech communication may increase the quality of speech and improve working efficiency in military aviation. Most of the developed algorithms can improve the quality of speech but unable to remove the background noise from the speech. This study provides researchers with a summary of the challenges in speech communication with background noise and provides research directions in the area of military personnel and workforces who work in noisy environments. Results of the study reveal that the DSP-based voice activity detection and background noise reduction algorithm reduced the spurious values of the speech signal.


DsPIC VAD DSC Voice activity detection Speech technology Speech processing Military communications 


  1. Ali, Z., & Talha, M. (2018). Innovative method for unsupervised voice activity detection and classification of audio segments, PF99. IEEE Access. Scholar
  2. Bhooshan, S., Kumar, V., Verma, U., Vatsyayan, H., & Rohit, K. (2008). T-Law: A new suggestion for signal companding. In 2008 Congress on Image and Signal Processing (Vol. 3, pp. 190–194).
  3. Bouguelia, M. R., Nowaczyk, S., Santosh, K. C., et al. (2018). Agreeing to disagree: Active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics, 9, 1307. Scholar
  4. Dey, N., & Ashour, A. S. (2018). Applied examples and applications of localization and tracking problem of multiple speech sources. In N. Dey, & A. S. Ashour (Eds.), Direction of arrival estimation and localization of multi-speech sources (pp. 35–48). Cham: Springer.CrossRefGoogle Scholar
  5. Dey, N., Ashour, A. S., Mohamed, W. S., & Nguyen, N. G. (2019). Acoustic sensors in biomedical applications. In N. Dey, A. S. Ashour, W. S. Mohamed, & N. G. Nguyen (Eds.), Acoustic sensors for biomedical applications (pp. 43–47). Cham: Springer.CrossRefGoogle Scholar
  6. Dey, N., Samanta, S., Yang, X.-S., Das, A., Chaudhuri, S. S. (2013). Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. International Journal of Bio-Inspired Computation, Inderscience Publishers, 5(5), 315–326.CrossRefGoogle Scholar
  7. dsPIC DSC Noise Suppression Library User’s Guide (2004-2011). Microchip Technology Inc, DS70133E. Retrieved from DeviceDoc/ DS-70133E.pdf.
  8. dsPIC33F Family Data Sheet, High-Performance, 16-bit Digital Signal Controllers, Microchip Technical Literature. Retrieved February 15, 2018, from en/DeviceDoc/70165d.pdf.
  9. G.711 Speech Encoding/Decoding Library for 16-bit MCUs and DSCs User’s Guide, 2011 Microchip Technology. Retrieved February 15, 2018, from
  10. Gao, X., Cao, H., Zhang, J., & Bai, J. (2013). A real-time DSP-based system for voice activity detection: Design and implement. International Journal of Signal Processing, Image Processing, and Pattern Recognition, 6(6), 27–40. Scholar
  11. García, M., Patiño, D., & Quintana, R. (2015). DSP implementation of the FxLMS algorithm for active noise control: Texas instruments TSM320C6713DSK, 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC).
  12. Graf, S., Herbig, T., Buck, M., Schmidt, G. (2016). Voice activity detection based on modulation-phase differences. In Proceedings of Speech Communication; 12. ITG Symposium. Retrieved from
  13. Haykin, S., & Moher, M. (2007). Introduction to analog & digital communications (2nd ed., pp. 207–208). Hoboken: John Wiley and Sons, Inc.Google Scholar
  14. Jie, L., & Datao, Y. (2017). Enhanced speech based jointly statistical probability distribution function for voice activity detection. Chinese Journal of Electronics, IET, 26(2), 325–330. Scholar
  15. Khoa P. C. (2012). Noise robust voice activity detection, Master thesis, The Nanyang Technological University, 2012. Retrieved from
  16. Kim, G., & Loizou, C. (2010). Improving speech intelligibility in noise using environment-optimized algorithms. IEEE Transactions on Audio, Speech, and Language Processing, 18(8), 2080–2090. Scholar
  17. Lahtinen, T. M., Huttunen, K. H., Kuronen, P. O., & Sorri, M., J. (2010). Radio speech communication problems reported in a survey of military pilots. Aviation, Space, and Environmental Medicine, 81(12), 1123–1127.CrossRefGoogle Scholar
  18. Lezzoum, N., Gagnon, G., & Voix, J. (2014). Voice activity detection system for smart earphones. IEEE Transactions on Consumer Electronics, 60(4), 737–744. Scholar
  19. Liang, J., Ahmad, M. O., & Swamy, M. N. S. (2005). Implementation of a voice activity detection and comfort noise generation Algorithm. In 48th Midwest Symposium on Circuits and Systems, Vol. 1, pp. 440–443.
  20. MPLAB integrated development environment. Retrieved February 15, 2018, from:
  21. Mukherjee, H., Obaidullah, S. M., Santosh, K. C., et al. (2018). Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal. International Journal of Speech Technology. Scholar
  22. New Microchip dsPIC33 Digital Signal Controller Family (2005). Retrieved from
  23. Pasad, A., Sabu, K., & Rao, P.(2017). Voice Activity detection for children’s read speech recognition in noisy conditions. In 2017 Twenty-third National Conference on Communications (NCC), IEEE.
  24. Pearce,D., & Hirsch, H. (2000). The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy condition. In aICSLP 2000, 6th International Conference on Spoken Language Processing. Beijing, China, 16–20 October 2000.Google Scholar
  25. Prell, C. G. L., & Clavier, O. H. (2017). Effects of noise on speech recognition: Challenges for communication by service members. Hearing Research, 349, (2017) 76–89. Scholar
  26. Price, M., Glass, J., & Chandrakasan, A. P. (2018). A low-power speech recognizer and voice activity detector using deep neural networks. IEEE Journal of Solid-state Circuits, 53(1), 66–75. Scholar
  27. Sat-Com (PTY) Ltd, Windhoek, Namibia,
  28. Sehgal, A., & Kehtarnavaz, K. (2018). A Convolutional neural network smartphone app for real-time voice activity detection. IEEE Access. Scholar
  29. Singh, R., Seltzer, M. L., Raj, B., & Stern, R. M. (2001). Speech in Noisy Environments: Robust automatic segmentation, feature extraction, and hypothesis combination. In February 2001 Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on 1, pp. 273–276.
  30. Speech Coding Solutions User’s Guide, DS70295A. (2007). Microchip Technology Inc. Retrieved February 15, 2018, from downloads/en/DeviceDoc/70295A.pdf, dsPIC® DSC.
  31. Smith, S.W. (2018), The breadth and depth of DSP-the roots of DSP, The Scientist and Engineer’s Guide to Digital Signal Processing. Retrieved April 11, 2018, from
  32. Vajda, S., & Santosh, K. C. (2017). A fast k-nearest neighbor classifier using unsupervised clustering. In K. Santosh, M. Hangarge, V. Bevilacqua & A. Negi (Eds.), Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016 (Vol. 709). Singapore: Springer.Google Scholar
  33. Yoo, I., Lim, H., & Yook, D. (2015). Formant-based robust voice activity detection. IEEE/ACM Transactions on audio, speech, and language Processing, 23(12), 2238–2245. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia
  2. 2.Sat-Com (PTY) LtdWindhoekNamibia

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