Top

International Journal of Speech Technology

Published in:

18-06-2018

Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal

Authors: Himadri Mukherjee, Sk. Md. Obaidullah, K. C. Santosh, Santanu Phadikar, Kaushik Roy

Published in: International Journal of Speech Technology | Issue 4/2018

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

search-config

AI-assisted search

Off

Abstract

Voice activity detection (VAD) refers to the task of identifying vocal segments from an audio clip. It helps in reducing the computational overhead as well elevate the recognition performance of speech-based systems by helping to discard the non vocal portions from an input signal. In this paper, a VAD technique is presented that uses line spectral frequency-based statistical features namely LSF-S coupled with extreme learning-based classification. The experiments were performed on a database of more than 350 h consisting of data from multifarious sources. We have obtained an encouraging overall accuracy of 99.43%.

next article Onset detection for tar solo

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

Retrieved Jan 24, 2018 from https://azure.microsoft.com/en-in/services/cognitive-services/speaker-recognition/.

Retrieved Jan 24, 2018 from https://www.nuance.com/omni-channel-customer-engagement/security/multi-modal-biometrics/freespeech.html.

Retrieved Jan 24, 2018 from https://www.youtube.com.

Asbai, N., Bengherabi, M., Amrouche, A., & Aklouf, Y. (2015). Improving the self-adaptive voice activity detector for speaker verification using map adaptation and asymmetric tapers. International Journal of Speech Technology, 18(2), 195–203.CrossRef

Bäckström, T. (2017). Speech coding with code-excited linear prediction: Signals and communication technology (1st ed.). New York: Springer. eBook ISBN 978-3-319-50204-5.

Beritelli, F., Casale, S., & Russo, M. (1999). A pattern recognition approach to robust voiced/unvoiced speech classification using fuzzy logic. International Journal of Pattern Recognition and Artificial Intelligence, 13(01), 109–132.CrossRef

Borin, R. G., & Silva, M. T. (2017). Voice activity detection using discriminative restricted Boltzmann machines. In EUSIPCO-2017 (pp. 523–527).

Bouguelia, M. R., Nowaczyk, S., Santosh, K. C., & Verikas, A. (2017). Agreeing to disagree: Active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-017-0645-0.

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(Jan), 1–30.MathSciNetMATH

Dey, M., Dey, N., Mahata, S. K., Chakraborty, S., Acharjee, S., & Das, A. (2014). Electrocardiogram feature based inter-human biometric authentication system. In ICESC-2014 (pp. 300–304).

Dharavath, K., Talukdar, F. A., Laskar, R. H., & Dey, N. (2017). Face recognition under dry and wet face conditions. In N. Dey & V. Santhi (Eds.), Intelligent techniques in signal processing for multimedia security (pp. 253–271). Cham: Springer.CrossRef

Ding, S., Zhang, N., Zhang, J., Xu, X., & Shi, Z. (2017). Unsupervised extreme learning machine with representational features. International Journal of Machine Learning and Cybernetics, 8(2), 587–595.CrossRef

Dudley, H. (1939). The vocoder. Bell Labs Record, 17, 122–126.

Dudley, H., Riesz, R. R., & Watkins, S. A. (1939). A synthetic speaker. Journal of Franklin Institute, 227, 739–764.CrossRef

Freeman, D. K., Cosier, G., Southcott, C. B., & Boyd, I. (1989). The voice activity detector for the Pan-European digital cellular mobile telephone service. In ICASSP-1989, (pp. 369–372).

Ghosh, P. K., Tsiartas, A., & Narayanan, S. (2011). Robust voice activity detection using long-term signal variability. IEEE Transactions on Audio, Speech, and Language Processing, 19(3), 600–613.CrossRef

Gil-Pita, R., Garca-Gomez, J., Bautista-Durn, M., Combarro, E., & Cocana-Fernandez, A. (2017). Evolved frequency log-energy coefficients for voice activity detection in hearing aids. In FUZZ-IEEE-2017 (pp. 1–6).

Gorriz, J. M., Ramrez, J., Lang, E. W., & Puntonet, C. G. (2006). Hard c-means clustering for voice activity detection. Speech Communication, 48(12), 1638–1649.CrossRef

Graf, S., Herbig, T., Buck, M., & Schmidt, G. (2015). Features for voice activity detection: A comparative analysis. EURASIP Journal on Advances in Signal Processing, 2015(1), 91.CrossRef

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.CrossRef

Hamaidi, L. K., Muma, M., & Zoubir, A. M. (2017). Robust distributed multi-speaker voice activity detection using stability selection for sparse non-negative feature extraction. In EUSIPCO-2017 (pp. 161–165).

Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 66(1), 51–83.CrossRef

Hu, K., Zhou, Z., Weng, L., Liu, J., Wang, L., Su, Y., et al. (2017). An optimization strategy for weighted extreme learning machine based on PSO. International Journal of Pattern Recognition and Artificial Intelligence, 31(01), 1751001.CrossRef

Huang, G. B., Bai, Z., Kasun, L. L. C., & Vong, C. M. (2015). Local receptive fields based extreme learning machine. IEEE Computational Intelligence Magazine, 10(2), 18–29.CrossRef

Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501.CrossRef

Hussain, T., Siniscalchi, S. M., Lee, C. C., Wang, S. S., Tsao, Y., & Liao, W. H. (2017). Experimental study on extreme learning machine applications for speech enhancement. IEEE Access, 5, 25542–25554.CrossRef

Joseph, S. M., & Babu, A. P. (2016). Wavelet energy based voice activity detection and adaptive thresholding for efficient speech coding. International Journal of Speech Technology, 19(3), 537–550.CrossRef

Luo, Y., Yang, B., Xu, L., Hao, L., Liu, J., Yao, Y., et al. (2017). Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-017-0678-4.

Lyon, D. A. (2009). The discrete Fourier transform, part 4: Spectral leakage. Journal of object technology. https://doi.org/10.5381/jot.2009.8.7.c2.

Ma, Y., & Nishihara, A. (2013). Efficient voice activity detection algorithm using long-term spectral flatness measure. EURASIP Journal on Audio, Speech, and Music Processing, 2013(1), 87.CrossRef

Mukherjee, H., Obaidullah, S. M., Phadikar, S., & Roy, K. (2018). MISNA-A musical instrument segregation system from noisy audio with LPCC-S features and extreme learning. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-018-5993-6.

Obaidullah, S. M., Santosh, K. C., Das, N., Halder, C., & Roy, K. (2018). Handwritten Indic script identification in multi-script document images: A survey. International Journal of Pattern Recognition and Artificial Intelligence. https://doi.org/10.1142/S0218001418560128.

Odelowo, B. O., & Anderson, D. V. (2017). Speech enhancement using extreme learning machines. In WASPAA-2017 (pp. 200–204).

Paliwal, K. K. (1992). On the use of line spectral frequency parameters for speech recognition. Digital Signal Processing, 2(2), 80–87.CrossRef

Pasad, A., Sabu, K., & Rao, P. (2017). Voice activity detection for children’s read speech recognition in noisy conditions. In NCC-2017 (pp. 1–6).

Rajeswari, P., Raju, S. V., Ashour, A. S., & Dey, N. (2017). Multi-fingerprint unimodelbased biometric authentication supporting cloud computing. In N. Dey & V. Santhi (Eds.), Intelligent techniques in signal processing for multimedia security (pp. 469–485). Cham: Springer.

Shi, Y. Q., Li, R. W., Zhang, S., Wang, S., & Yi, X. Q. (2016). A speech endpoint detection algorithm based on BP neural network and multiple features. In AMMIS-2015 (pp. 393–402).

Solé-Casals, J., Martí-Puig, P., Reig-Bolaño, R., & Zaiats, V. (2009). Score function for voice activity detection. In NOLISP-09 (pp. 76–83).

Vajda, S., & Santosh, K. C. (2016). A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering. In RTIP2R-2016 (pp. 185–193).

Wang, L., Phapatanaburi, K., Go, Z., Nakagawa, S., Iwahashi, M., & Dang, J. (2017). Phase aware deep neural network for noise robust voice activity detection. In ICME-17 (pp. 1087–1092).

Wei, H., Long, Y., & Mao, H. (2016). Improvements on self-adaptive voice activity detector for telephone data. International Journal of Speech Technology, 19(3), 623–630.CrossRef

Wu, B., Ren, X., Liu, C., & Zhang, Y. (1997). A robust, real-time voice activity detection algorithm for embedded mobile devices. Journal of Sol-Gel Science and Technology, 8(2), 133–146.CrossRef

Wu, G. D., & Wu, P. J. (2016). Type-2 fuzzy neural network for voice activity detection. In iFuzzy-2016 (pp. 1–4).

Wu, J., & Zhang, X. L. (2011). An efficient voice activity detection algorithm by combining statistical model and energy detection. EURASIP Journal on Advances in Signal Processing, 2011(1), 18.CrossRef

Yoo, I. C., Lim, H., & Yook, D. (2015). Formant-based robust voice activity detection. IEEE/ACM Transactions on Audio, Speech and Language Processing, 23(12), 2238–2245.CrossRef

Zhao, H., Guo, X., Wang, M., Li, T., Pang, C., & Georgakopoulos, D. (2018). Analyze EEG signals with extreme learning machine based on PMIS feature selection. International Journal of Machine Learning and Cybernetics, 9(2), 243–249.CrossRef

Title: Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal
Authors: Himadri Mukherjee
Sk. Md. Obaidullah
K. C. Santosh
Santanu Phadikar
Kaushik Roy
Publication date: 18-06-2018
Publisher: Springer US
Published in: International Journal of Speech Technology / Issue 4/2018
Print ISSN: 1381-2416
Electronic ISSN: 1572-8110
DOI: https://doi.org/10.1007/s10772-018-9525-6

Springer Professional

Abstract

Please log in to get access to your license.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2018

Three-stage speaker verification architecture in emotional talking environments

DSP-based voice activity detection and background noise reduction

An investigation on the degradation of different features extracted from the compressed American English speech using narrowband and wideband codecs

Reduction of residual noise based on eigencomponent filtering for speech enhancement

Mel scaled M-band wavelet filter bank for speech recognition

Effective use of combined excitation source and vocal-tract information for speaker recognition tasks