Top

International Journal of Speech Technology

Published in:

27-07-2016

Robust acoustic bird recognition for habitat monitoring with wireless sensor networks

Authors: Amira Boulmaiz, Djemil Messadeg, Noureddine Doghmane, Abdelmalik Taleb-Ahmed

Published in: International Journal of Speech Technology | Issue 3/2016

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

search-config

AI-assisted search

Off

Abstract

The key solution to study birds in their natural habitat is the continuous survey using wireless sensors networks (WSN). The final objective of this study is to conceive a system for monitoring threatened bird species using audio sensor nodes. The principal feature for their recognition is their sound. The main limitations encountered with this process are environmental noise and energy consumption in sensor nodes. Over the years, a variety of birdsong classification methods has been introduced, but very few have focused to find an adequate one for WSN. In this paper, a tonal region detector (TRD) using sigmoid function is proposed. This approach for noise power estimation offers flexibility, since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Once the tonal regions in the noisy bird sound are detected, the features gammatone teager energy cepstral coefficients (GTECC) post-processed by quantile-based cepstral normalization were extracted from the above signals for classification using deep neural network classifier. Experimental results for the identification of 36 bird species from Tonga lake (northeast of Algeria) demonstrate that the proposed TRD–GTECC feature is highly effective and performs satisfactorily compared to popular front-ends considered in this study. Moreover, recognition performance, noise immunity and energy consumption are considerably improved after tonal region detection, indicating that it is a very suitable approach for the acoustic bird recognition in complex environments with wireless sensor nodes.

previous article Improvements on self-adaptive voice activity detector for telephone data

next article Corpus based part-of-speech tagging

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

Aissaoui, R., Tahar, A., Saheb, M., Guergueb, L., & Houhamdi, M. (2011). Diurnal behaviour of Ferruginous Duck Aythya nyroca wintering at the El-Kala wetlands (Northeast Algeria). Bulletin de l’Institut Scientifique, Rabat, section Sciences de la Vie, 33(2), 67–75.

Bardeli, R., Wolff, D., Kurth, F., Koch, M., Tauchert, K. H., & Frommolt, K. H. (2010). Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letters, 31(12), 1524–1534.CrossRef

Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. Acoustics, Speech and Signal Processing, IEEE Transactions on, 27(2), 113–120.CrossRef

Bořil, H., & Hansen, J. H. (2010). Unsupervised equalization of Lombard effect for speech recognition in noisy adverse environments. Audio, Speech, and Language Processing, IEEE Transactions on, 18(6), 1379–1393.CrossRef

Bořil, H., & Hansen, J. H. (2011). UT-Scope: Towards LVCSR under Lombard effect induced by varying types and levels of noisy background. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on (pp. 4472–4475). IEEE.

Brumm, H. (2004). The impact of environmental noise on song amplitude in a territorial bird. Journal of Animal Ecology, 73(3), 434–440.CrossRef

Chettibi, F., Khelifa, R., Aberkane, M., Bouslama, Z., & Houhamdi, M. (2013). Diurnal activity budget and breeding ecology of the White-headed Duck Oxyura leucocephala at Lake Tonga (North-east Algeria). Zoology and Ecology, 23(3), 183–190.CrossRef

Chu, W., & Blumstein, D. T. (2011). Noise robust bird song detection using syllable pattern-based hidden Markov models. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on (pp. 345–348). IEEE.

Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification. Neural Networks, 32, 333–338.CrossRef

Cohen, I. (2003). Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging. Speech and Audio Processing, IEEE Transactions on, 11(5), 466–475.CrossRef

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1), 30–42.CrossRef

Davis, S. B., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. Acoustics, Speech and Signal Processing, IEEE Transactions on, 28(4), 357–366.CrossRef

De Oliveira, A. G., Ventura, T. M., Ganchev, T. D., de Figueiredo, J. M., Jahn, O., Marques, M. I., et al. (2015). Bird acoustic activity detection based on morphological filtering of the spectrogram. Applied Acoustics, 98, 34–42.CrossRef

Deng, L., Hinton, G., & Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: An overview. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 8599–8603). IEEE.

Deng, L., Yu, D., & Platt, J. (2012). Scalable stacking and learning for building deep architectures. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2133–2136). IEEE.

Dharanipragada, S., & Padmanabhan, M. (2000). A nonlinear unsupervised adaptation technique for speech recognition. In INTERSPEECH (pp. 556–559).

Gerkmann, T., & Hendriks, R. C. (2012). Improved MMSE-based noise PSD tracking using temporal cepstrum smoothing. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 105–108). IEEE.

Ghitza, O. (1994). Auditory models and human performance in tasks related to speech coding and speech recognition. Speech and Audio Processing, IEEE Transactions on, 2(1), 115–132.CrossRef

Glasberg, B. R., & Moore, B. C. (1990). Derivation of auditory filter shapes from notched-noise data. Hearing Research, 47(1), 103–138.CrossRef

Gros-Desormeaux, H., Vidot, N., & Hunel, P. (2010). Wildlife assessment using wireless sensor networks. Rijeka: INTECH Open Access Publisher.CrossRef

Hilger, F., & Ney, H. (2006). Quantile based histogram equalization for noise robust large vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 14(3), 845–854.CrossRef

Hill, J., & Culler, D. (2002). A wireless embedded sensor architecture for system-level optimization. UC Berkeley Technical Report.

Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine IEEE, 29(6), 82–97.CrossRef

Irino, T., & Patterson, R. D. (1997). A time-domain, level-dependent auditory filter: The gammachirp. The Journal of the Acoustical Society of America, 101(1), 412–419.CrossRef

Jančovič, P., & Köküer, M. (2011). Automatic detection and recognition of tonal bird sounds in noisy environments. EURASIP Journal on Advances in Signal Processing, 2011(1), 982936.CrossRef

Kaiser, J. F. (1990). On a simple algorithm to calculate the energy’of a signal. In Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on (pp. 381–384).

Kim, C., & Stern, R. M. (2008). Robust signal-to-noise ratio estimation based on waveform amplitude distribution analysis. In INTERSPEECH (pp. 2598–2601).

Kim, C., & Stern, R. M. (2012). Power-normalized cepstral coefficients (PNCC) for robust speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 4101–4104). IEEE.

Kortelainen, J., & Noponen, K. (2005). Neural Networks, Intelligent Systems. Reading: Addison-Wesley Publishing Co.

Laibowitz, M., Gips, J., Aylward, R., Pentland, A., & Paradiso, J. A. (2006). A sensor network for social dynamics. In Proceedings of the 5th international conference on Information processing in sensor networks (pp. 483–491). ACM.

Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (pp. 88–97). ACM.

Maragos, P., Kaiser, J. F., & Quatieri, T. F. (1993). On amplitude and frequency demodulation using energy operators. IEEE Transactions on signal processing, 41(4), 1532–1550.CrossRefMATH

McIlraith, A. L., & Card, H. C. (1997). Bird song identification using artificial neural networks and statistical analysis. In Electrical and Computer Engineering, 1997. Engineering Innovation: Voyage of Discovery. IEEE 1997 Canadian Conference on (Vol. 1, pp. 63–66). IEEE.

Olguín, D. O., & Pentland, A. S. (2008). Social sensors for automatic data collection. In AMCIS 2008 Proceedings, p. 171.

Patil, H., & Parhi, K. K. (2010). Novel variable length Teager energy based features for person recognition from their hum. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on (pp. 4526–4529). IEEE.

Patterson, R. D., Robinson, K., Holdsworth, J., McKeown, D., Zhang, C., & Allerhand, M. (1992). Complex sounds and auditory images. Auditory Physiology and Perception, 83, 429–446.CrossRef

Patti, A., & Williamson, G. (2013). Methods for classification of nocturnal migratory bird vocalizations using Pseudo Wigner-Ville Transform. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 758–762). IEEE.

Potamitis, I. (2015). Unsupervised dictionary extraction of bird vocalisations and new tools on assessing and visualising bird activity. Ecological Informatics, 26, 6–17.CrossRef

Prasad, N. V., & Umesh, S. (2013). Improved cepstral mean and variance normalization using Bayesian framework. In Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop on (pp. 156–161). IEEE.

Ptacek, L., Machlica, L., Linhart, P., Jaska, P., & Muller, L. (2015). Automatic recognition of bird individuals on an open set using as-is recordings. Bioacoustics, 25(1), 1–19.

Rangachari, S., & Loizou, P. C. (2006). A noise-estimation algorithm for highly non-stationary environments. Speech Communication, 48(2), 220–231.CrossRef

Sadjadi, S. O., Bořil, H., & Hansen, J. H. (2012). A comparison of front-end compensation strategies for robust LVCSR under room reverberation and increased vocal effort. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 4701–4704). IEEE.

Sainath, T. N., Mohamed, A. R., Kingsbury, B., & Ramabhadran, B. (2013). Deep convolutional neural networks for LVCSR. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 8614–8618). IEEE.

Siniscalchi, S. M., Yu, D., Deng, L., & Lee, C. H. (2013). Exploiting deep neural networks for detection-based speech recognition. Neurocomputing, 106, 148–157.CrossRef

Slaney, M. (1998). Auditory toolbox. Interval Research Corporation, Technical Report (Vol. 10).

Stattner, E. (2012). Contributions à l’étude des réseaux sociaux: propagation, fouille, collecte de données (Doctoral dissertation, Université des Antilles-Guyane).

Stattner, E., Hunel, P., Vidot, N., & Collard, M. (2011). Acoustic scheme to count bird songs with wireless sensor networks. In World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE International Symposium on a(pp. 1–3). IEEE.

Stowell, D., & Plumbley, M. D. (2014). Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ, 2, e488.CrossRef

Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., & Estrin, D. (2004). Habitat monitoring with sensor networks. Communications of the ACM, 47(6), 34–40.CrossRef

Trifa, V., Girod, L., Collier, T. C., Blumstein, D., & Taylor, C. E. (2007). Automated wildlife monitoring using self-configuring sensor networks deployed in natural habitats. Center for Embedded Network Sensing.

Ventura, T. M., de Oliveira, A. G., Ganchev, T. D., de Figueiredo, J. M., Jahn, O., Marques, M. I., et al. (2015). Audio parameterization with robust frame selection for improved bird identification. Expert Systems with Applications, 42(22), 8463–8471.CrossRef

Wang, H., Estrin, D., & Girod, L. (2003). Preprocessing in a Tiered Sensor Network for Habitat Monitoring, EURASIP. Journal on Applied Signal Processing, 4, 392–401.CrossRef

Weninger, F., & Schuller, B. (2011). Audio recognition in the wild: Static and dynamic classification on a real-world database of animal vocalizations. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on (pp. 337–340). IEEE.

Yapanel, U. H., & Hansen, J. H. (2008). A new perceptually motivated MVDR-based acoustic front-end (PMVDR) for robust automatic speech recognition. Speech Communication, 50(2), 142–152.CrossRef

Yong, P. C., Nordholm, S., & Dam, H. H. (2012). Noise estimation based on soft decisions and conditional smoothing for speech enhancement. In Acoustic Signal Enhancement; Proceedings of IWAENC 2012; International Workshop on (pp. 1–4). VDE.

Yoshizawa, S., Hayasaka, N., Wada, N., & Miyanaga, Y. (2004). Cepstral gain normalization for noise robust speech recognition. In Acoustics, Speech, and Signal Processing, 2004 (ICASSP’04). IEEE International Conference on (Vol. 1, pp. I–209). IEEE.

Yu, R. (2009). A low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on (pp. 4421–4424). IEEE.

Yu, D., Deng, L., Seide, F. T. B., & Li, G. (2016). U.S. Patent No. 9,235,799. Washington, DC: U.S. Patent and Trademark Office.

Zhang, X., & Li, Y. (2015). Adaptive energy detection for bird sound detection in complex environments. Neurocomputing, 155, 108–116.CrossRef

Title: Robust acoustic bird recognition for habitat monitoring with wireless sensor networks
Authors: Amira Boulmaiz
Djemil Messadeg
Noureddine Doghmane
Abdelmalik Taleb-Ahmed
Publication date: 27-07-2016
Publisher: Springer US
Published in: International Journal of Speech Technology / Issue 3/2016
Print ISSN: 1381-2416
Electronic ISSN: 1572-8110
DOI: https://doi.org/10.1007/s10772-016-9354-4

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 3/2016

Text-dependent speaker verification using classical LBG, adaptive LBG and FCM vector quantization

Performance of speaker identification using CSM and TM

Automatic genre classification of Indian Tamil and western music using fractional MFCC

Wavelet energy based voice activity detection and adaptive thresholding for efficient speech coding

Erratum to: What we have and what is needed, how to evaluate Arabic Speech Synthesizer?

Audio steganalysis using deep belief networks