CNC Mechanical Machine and Musical Sound Analysis of Zero Crossing Rates (ZCR) by Artificial Intelligence Based Tools.

Islam Md Shafikul1

1

Publication Date: 2024/08/24

Abstract: In our regular lives, sound plays an important role on various sides. There is a valuable effect on communications, emotions, and affections. Humans and animals are not the only sources of sounds. Machines and engines also generate a wide range of sounds. Every sound has different characteristics according to its internal format. Sound source and production method are the key factors in these differences. In our article, we showed the differences in zero crossing rates between mechanical machines (CNC milling) and music sounds using the artificial intelligence-based tool LibROSA. At the end of the results, we estimate that the human or musical voice has a lower zero crossing rate than mechanical machine sounds.

Keywords: Sound Analysis, CNC Milling Machine, Artificial Intelligence, Sound Zero Crossing Rate (ZCR).

DOI: https://doi.org/10.38124/ijisrt/IJISRT24JUL1771

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JUL1771.pdf

REFERENCES

  1. J. K. Lee, C. D. Yoo, “Wavelet speech enhancementbased on voiced/unvoiced decision”, Korea AdvancedInstitute of Science and Technology The 32ndInternational Congress and Exposition on NoiseControl Engineering, Jeju International ConventionCenter, Seogwipo, Korea, August 25-28, 2003.
  2. B. Atal, and L. Rabiner, “A Pattern RecognitionApproach to Voiced-Unvoiced-Silence Classificationwith Applications to Speech Recognition,” IEEETrans. On ASSP, vol. ASSP-24, pp. 201-212, 1976.
  3. S. Ahmadi, and A.S. Spanias, “Cepstrum-Based PitchDetection using a New Statistical V/UV ClassificationAlgorithm,” IEEE Trans. Speech Audio Processing,vol. 7 No. 3, pp. 333-338, 1999.
  4. Y. Qi, and B.R. Hunt, “Voiced-Unvoiced-SilenceClassifications of Speech using Hybrid Features and aNetwork Classifier,” IEEE Trans. Speech AudioProcessing, vol. 1 No. 2, pp. 250-255, 1993.
  5. L. Siegel, “A Procedure for using Pattern ClassificationTechniques to obtain a Voiced/Unvoiced Classifier”,IEEE Trans. on ASSP, vol. ASSP-27, pp. 83- 88, 1979.
  6. T.L. Burrows, “Speech Processing with Linear andNeural Network Models”, Ph.D. thesis, CambridgeUniversity Engineering Department, U.K., 1996.
  7. D.G. Childers, M. Hahn, and J.N. Larar, “Silent andVoiced/Unvoiced/Mixed Excitation (Four-Way)Classification of Speech,” IEEE Trans. on ASSP, vol. 37 No. 11, pp. 1771-1774, 1989.
  8. J. K. Shah, A. N. Iyer, B. Y. Smolenski, and R. E.Yantorno “Robust voiced/unvoiced classification usingnovel features and Gaussian Mixture model”, SpeechProcessing Lab., ECE Dept., Temple University, 1947N 12th St., Philadelphia, PA 19122-6077, USA.
  9. J. Marvan, “Voice Activity detection Method andApparatus for voiced/unvoiced decision and PitchEstimation in a Noisy speech feature extraction”,08/23/2007, United States Patent 20070198251.
  10. T. F. Quatieri, Discrete-Time Speech SignalProcessing: Principles and Practice, MIT LincolnLaboratory, Lexington, Massachusetts, Prentice Hall,2001, ISBN-13:9780132429429.
  11. F.J. Owens, Signal Processing of Speech, McGrawHill, Inc., 1993, ISBN-0-07-04795550.
  12. L. R. Rabiner, and R. W. Schafer, Digital Processing ofSpeech Signals, Englewood Cliffs, New Jersey,Prentice Hall, 512-ISBN-13:9780132136037, 1978.