Skip to main content
SpringerLink
Log in

Robust scream sound detection via sound event partitioning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a robust scream-sound detection scheme for acoustic surveillance applications. To enhance the discriminability between scream and non-scream sounds, a sound-event partitioning (SEP) method that facilitates the extraction of multiple acoustic vectors from a single sound event is developed. Regularized principal component analysis (PCA) and normalization are applied to the acoustic vectors, which are then classified by support vector machines (SVMs). Experimental results based on 1000 sound events show that the proposed scheme is effective even if there are severe mismatches between the training and testing conditions. The experimental results also show that the proposed scheme can reduce the equal error rate (EER) by up to 60 % when compared to a classical approach that uses mel-frequency cepstral coefficients (MFCC) as features. Extensive analyses on different processing stages of the proposed sound detection scheme also suggest that sound partitioning and feature normalization play important roles in boosting the detection performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. It is important to note that individual frames do not contain sufficient information for differentiating scream and non-scream sounds. In fact, individual frames of scream and non-scream sound are highly overlapped in the feature space, which will cause problems if they are directly used for training SVM classifiers.

References

  1. addnoise. http://www.mathworks.com/matlabcentral/fileexchange/32136-add-noise/content/addnoise/addnoise.m

  2. Ali S, Smith-Miles KA (2006) Improved support vector machine generalization using normalized input space. In: Proc. of 19th Australian Joint Conference on Artificial Intelligence. pp 362–371

  3. Atrey PK, Maddage NC, Kankanhalli MS (2006) Audio based event detection for multimedia surveillance. In: Proc.of IEEE International Conference on Acoustics, Speech and Signal Processing. pp V813-V816

  4. Chu S, Narayanan S, Kuo CCJ (2009) Environmental sound recognition with time-frequency audio features. IEEE Trans Audio, Speech Lang Process 17(6):1142–1158

    Article  Google Scholar 

  5. Clavel C, Ehrette T, Richard G (2005) Events detection for an audio-based surveillance system. In: Proc.of IEEE International Conference on Multimedia and Expo. pp 1306–1309

  6. Davis S, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Signal Process 28(4):357–366

    Article  Google Scholar 

  7. Dennis J, Tran HD, Chng E-S (2013) Image feature representation of the subband power distribution for robust sound event classification. IEEE Trans Audio, Speech Lang Process 21(2):367–377

    Article  Google Scholar 

  8. Dennis J, Tran HD, Chng ES (2013) Overlapping sound event recognition using local spectrogram features and the generalised hough transform. Pattern Recogn Lett 34(9):1085–1093

    Article  Google Scholar 

  9. Dennis J, Tran HD, Li H (2011) Spectrogram image feature for sound event classification in mismatched conditions. IEEE Signal Process Lett 18(2):130–133

    Article  Google Scholar 

  10. Ferrer L, Bratt H, Burget L, Cernocky H, Glembek O, Graciarena M, Lawson A, Lei Y, Matejka P, Plchot O (2011) Promoting robustness for speaker modeling in the community: the PRISM evaluation set. In: Proc.of NIST 2011 Workshop

  11. Ghoraani B, Krishnan S (2011) Time-frequency matrix feature extraction and classification of environmental audio signals. IEEE Trans Audio, Speech Lang Process 19(7):2197–2209

    Article  Google Scholar 

  12. Guo G, Li SZ (2003) Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw 14(1):209–215

    Article  Google Scholar 

  13. Hautamaki V, Kinnunen T, Sedlak F, Lee KA, Ma B, Li H (2013) Sparse classifier fusion for speaker verification. IEEE Trans Audio, Speech Lang Process 21(8):1622–1631

    Article  Google Scholar 

  14. Huang W, Chiew T-K, Li H, Kok TS, Biswas J (2010) Scream detection for home applications. In: Proc.of 6th IEEE Conference on Industrial Electronics and Applications. pp 2115–2120

  15. Human Sound Effects. http://www.sound-ideas.com/

  16. Jégou H, Chum O (2012) Negative evidences and co-occurences in image retrieval: the benefit of PCA and whitening. In: Proc.of European Conference on Computer Vision. pp 774–787

  17. Kim MJ, Kim H (2011) Automatic extraction of pornographic contents using radon transform based audio features. In: Prof. of 9th International Workshop onContent-Based Multimedia Indexing. pp 205–210

  18. Kinnunen T, Li H (2010) An overview of text-independent speaker recognition: from features to supervectors. Speech Comm 52(1):12–40

    Article  Google Scholar 

  19. Kotus J, Lopatka K, Czyzewski A (2014) Detection and localization of selected acoustic events in acoustic field for smart surveillance applications. Multimedia Tools Appl 68(1):5–21

    Article  Google Scholar 

  20. Lei B, Rahman SA, Song I (2014) Content-based classification of breath sound with enhanced features. Neurocomputing 141:139–147

    Article  Google Scholar 

  21. Liao W-H, Lin Y-K (2009) Classification of non-speech human sounds: Feature selection and snoring sound analysis. In: Proc. of IEEE International Conference on on Systems, Man and Cybernetics. pp 2695–2700

  22. Mak M-W, Kung S-Y (2012) Low-power SVM classifiers for sound event classification on mobile devices. In: Proc.of IEEE International Conference on Acoustics, Speech and Signal Processing pp 1985–1988

  23. Mak M-W, Rao W (2011) Utterance partitioning with acoustic vector resampling for GMM–SVM speaker verification. Speech Comm 53(1):119–130

    Article  Google Scholar 

  24. Mak M-W, Yu H-B (2014) A study of voice activity detection techniques for NIST speaker recognition evaluations. Comput Speech Lang 28(1):295–313

    Article  Google Scholar 

  25. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval, vol 1. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  26. Martin A, Doddington G, Kamm T, Ordowski M, Przybocki M (1997) The DET curve in assessment of detection task performance. In: Proc.of 5th European Conference on Speech Communication and Technology. pp 1895–1898

  27. Ntalampiras S, Potamitis I, Fakotakis N (2009) On acoustic surveillance of hazardous situations. In: Proc.of IEEE International Conference on Acoustics, Speech and Signal Processing. pp 165–168

  28. Penet C, Demarty C-H, Gravier G, Gros P (2014) Variability modelling for audio events detection in movies. Multimedia Tools and Applications 1–31

  29. PRISM-SET. https://code.google.com/p/prism-set/

  30. Ralf H, Thore G (2002) A PAC-Bayesian margin bound for linear classifiers. IEEE Trans Inf Theory 48(12):3140–3150

    Article  MathSciNet  MATH  Google Scholar 

  31. Rao W, Mak M-W (2013) Boosting the performance of i-vector based speaker verification via utterance partitioning. IEEE Trans Audio, Speech Lang Process 21(5):1012–1022

    Article  Google Scholar 

  32. rir. http://sgm-audio.com/research/rir/rir.html

  33. Sánchez J, Perronnin F, Mensink T, Verbeek J (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245

    Article  MathSciNet  MATH  Google Scholar 

  34. Simonyan K, Parkhi OM, Vedaldi A, Zisserman A (2013) Fisher Vector Faces in the Wild. In: Proc. of British Machine Vision Conference. pp 8.1-8.12

  35. Tran HD, Li H (2011) Sound event recognition with probabilistic distance SVMs. IEEE Trans Audio, Speech Lang Process 19(6):1556–1568

    Article  Google Scholar 

  36. Valenzise G, Gerosa L, Tagliasacchi M, Antonacci F, Sarti A (2007) Scream and gunshot detection and localization for audio-surveillance systems. In: Proc.of IEEE Conference on Advanced Video and Signal Based Surveillance. pp 21–26

  37. Varga A, Steeneken HJM (1993) Assessment for automatic speech recognition: II. NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems. Speech Comm 12(3):247–251

    Article  Google Scholar 

  38. Wang Y, Han K, Wang D (2013) Exploring monaural features for classification-based speech segregation. IEEE Trans Audio, Speech Lang Process 21(2):270–279

    Article  Google Scholar 

  39. Zhao X, Shao Y, Wang D (2012) CASA-based robust speaker identification. IEEE Trans Audio, Speech Lang Process 20(5):1608–1616

    Article  Google Scholar 

  40. Zhao X, Wang D (2013) Analyzing noise robustness of MFCC and GFCC features in speaker identification. In: Proc.of IEEE International Conference on Acoustics, Speech and Signal Processing. pp 7204–7208

Download references

Acknowledgments

The work was supported partly by National Natural Science Foundation of China (No. 61402296), Motorola Solutions Foundation (ID: 7186445) and the Hong Kong Polytechnic University Grant No. G-YL78. The authors would like to thank Wing-Lung Leung for developing the sound recording system and part of the Android App.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, and Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Baiying Lei

  2. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Man-Wai Mak

Authors
  1. Baiying Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Man-Wai Mak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baiying Lei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lei, B., Mak, MW. Robust scream sound detection via sound event partitioning. Multimed Tools Appl 75, 6071–6089 (2016). https://doi.org/10.1007/s11042-015-2555-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2555-z

Keywords

Search

Navigation