Skip to main content
SpringerLink
Log in

Recognizing Quality of Floor Tiling from Knocking Signals Using HMMs

  • Conference paper
  • First Online:
Recent Advances in Information and Communication Technology 2017 (IC2IT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 566))

Included in the following conference series:

Abstract

Property buyers and homeowners throughout the world have discovered badly tiled floors in their buildings. Consequently, they spend time on expensive repairs because their tiling has a shorter than expected lifetime. If it were possible to ascertain the quality of floor tiling before making a payment, this problem could be solved. Usually, it is difficult to determine the quality of floor tiling visually. Therefore, this work proposes a non-destructive method of identifying correctly and incorrectly laid flooring using tile knocking signals. Acoustic models were created using Mel Frequency Cepstral Coefficients (MFCCs) and Hidden Markov Models (HMMs). The sounds resulting from firmly or gently knocking on tiles, and the characteristics of knocking signals were used for the acoustic model training and testing. Regardless of firm or gentle knocking, the results showed that the proposed method could distinguish between correctly and incorrectly laid floor tiles accurately and efficiently.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Haff, R.P., Slaughter, D.C., Sarig, Y., Kader, A.: X-ray assessment of translucency in pineapple. J. Food Process. Preserv. 30, 527–533 (2006)

    Article  Google Scholar 

  2. Tangruamsub, S., Punyabukkana, P., Suchato, A.: Thai speech keyword spotting using heterogeneous acoustic modeling. In: IEEE International Conference on Research, Innovation and Vision for the Future, pp. 253–260 (2007)

    Google Scholar 

  3. Tangwongsan, S., Phoophuangpairoj, R.: Boosting Thai syllable speech recognition using acoustic models combination. In: International Conference on Computer and Electrical Engineering, pp. 568–572 (2008)

    Google Scholar 

  4. Tangwongsan, S., Po-Aramsri, P., Phoophuangpairoj, R.: Highly efficient and effective techniques for Thai syllable speech recognition. In: Maher, M.J. (ed.) Advances in Computer Science – ASIAN 2004 Higher Level Decision Making. ASIAN2004. LNCS, vol. 3321, pp. 259–270. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Li, F., Ma, J., Huang, D.: MFCC and SVM based recognition of Chinese Vowels. In: Hao, Y., et al. (eds.) Computational Intelligence and Security. CIS 2005. LNCS, vol. 3802, pp. 812–819. Springer, Heidelberg (2005)

    Google Scholar 

  6. Phoophuangpairoj, R.: Using multiple HMM recognizers and the maximum method to improve voice-controlled robots. In: International Conference on Intelligent Signal Processing and Communication Systems, pp. 1–6 (2011)

    Google Scholar 

  7. Ting, H., Yingchun, Y., Zhaohui, W.: Combining MFCC and pitch to enhance the performance of the gender recognition. In: 8th International Conference on Signal Processing (2006)

    Google Scholar 

  8. Phoophuangpairoj, R.: Automated classification of watermelon quality using non-flicking reduction and HMM sequences derived from flicking sound characteristics. J. Inf. Sci. Eng. 30(4), 1015–1033 (2014)

    Google Scholar 

  9. Phoophuangpairoj, R.: Durian ripeness striking sound recognition using N-gram models with N-best lists and majority voting. In: Boonkrong, S., Unger, H., Meesad, P. (eds.) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol. 265, pp. 167–176. Springer, Cham (2014)

    Chapter  Google Scholar 

  10. Phoophuangpairoj, R., Srikun, N.: Computerized recognition of pineapple grades using physicochemical properties and flicking sounds. Int. J. Agric. Biol. Eng. 7(3), 93–101 (2014)

    Google Scholar 

  11. Eren, E., Kurama, S., Solodov, I.: Characterization of porosity and defect imaging in ceramic tile using ultrasonic inspections. Ceram. Int. 38, 2145–2151 (2012)

    Article  Google Scholar 

  12. Hocenski, Z., Matić, T., Vidović, I.: Technology transfer of computer vision defect detection to ceramic tiles industry. In: International Conference on Smart Systems and Technologies (SST), pp. 301–305 (2016)

    Google Scholar 

  13. Mohan, V., Kumar, S.S.: An automated tiles defect detection. Int. J. Comput. Appl. 109(11), 24–27 (2015)

    Google Scholar 

  14. Karimi, M.H., Asemani, D.: Surface defect detection in tiling industries using digital image processing methods: analysis and evaluation. ISA Trans. 53, 834–844 (2014)

    Article  Google Scholar 

  15. Young, S. et al.: The HTK Book (for HTK Version 3.4), December 2006

    Google Scholar 

  16. The Hidden Markov Model Toolkit (HTK). http://htk.eng.cam.ac.uk/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Engineering, Rangsit University, Bangkok, Thailand

    Rong Phoophuangpairoj

Authors
  1. Rong Phoophuangpairoj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong Phoophuangpairoj .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Phoophuangpairoj, R. (2018). Recognizing Quality of Floor Tiling from Knocking Signals Using HMMs. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60663-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60662-0

  • Online ISBN: 978-3-319-60663-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation