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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 53 issue 7 (july 2019) : 864-869,   Doi: 10.18805/ijar.B-3601

Discrimination of karan fries cow’s individuality by the mean of their vocal acoustic features

R
Rohit Gupta
S
Surendra S. Lathwal
P
Pragya Bhadauria
S
Shilpi Kerketta
A
Ahmad Fahim
I
Indu Devi
Y
Y.S. Jadoun
1Punjab Agriculture University, Ludhiana-141004, Punjab, India.
Cite article:- Gupta Rohit, Lathwal S. Surendra, Bhadauria Pragya, Kerketta Shilpi, Fahim Ahmad, Devi Indu, Jadoun Y.S. (2018). Discrimination of karan fries cow’s individuality by the mean of their vocal acoustic features. Indian Journal of Animal Research. 53(7): 864-869. doi: 10.18805/ijar.B-3601.

ABSTRACT

Sound is one of the most important means of conveying information over long distances as well as in close vicinity. Utterance of animals becomes unique for them when they communicate their individuality or physiological state to the other co species partner. Present study was based on hypothesis of discrimination of individual identity through vocal signal of lactating karan fries (KF) cows. For this 25 KF cows were selected for recording of their vocal signal. Vocal call was recorded after separation of animal from their living herd in the morning hours.  Acoustic features of vocalization of individual cow were extracted with the help of PRAAT acoustic analysis software. Analysis of all acoustic features extracted from 250 voice samples of 25 KF cows revealed that differences for amplitude, Total energy, pitch, intensity, formants, pulse, periods, unvoiced frames, voice breaks, jitter, shimmer, mean noise/harmonic ratio and mean harmonic/noise ratio were found highly significant (p < 0.001). Out of these only few acoustic features viz. pulse, pitch, jitter, shimmer, voice break and formants were observed to have significant (p<0.05) difference between each and every individual KF cow. Among these feature formants frequency of every individual cow had a unique pattern in the distribution of frequency contour in their vocal spectrogram. Study concluded that vocal signal of KF cow have some unique feature at individual level, by which cow identification could be made. 

REFERENCES

  1. Clemins, P.J., Johnson, M.T., Leong, K.M. and Savage, A. (2005). Automatic classification and speaker identification of African elephant (loxodonta Africana) vocalization. J. Acoust. Soc. Am. 17: 23-27
  2. Fant, G. (1960). Acoustic theory of speech production. The Hague: Mouton. PP: 12.
  3. Fitch, W.T. and Hauser, M.D. (1995). Vocal production in non-human primates: acoustics, physiology, and functional con- straints on ‘honest’ advertisement. Am. J. Primatol. 37:191–219.
  4. Harvey, W.B. (1987). Users guide for LSMLMW, Mixed Model Least Squares and Maximum Likelihood Computer Programme. PC-    I version, mimeograph. Ohio State university, OH, USA, PP: 06
  5. Hauser, M. D. (1996). The Evolution of communication. MIT Press, Cambridge, PP: 27
  6. Ikeda, Y. and Ishii, Y. (2008). Recognition of two psychological condition of a single cow by her voice. Compt. and Elect. in Agri., 62:67-72
  7. Ikeda, Y., Jahns, G., Kowalczyk, W. and Walter, K. (2000). Acoustic Analysis to Recognize Individuals and Animal Conditions. In Proceddings of the XIV Memorial CIRG World Congress, PP: 220-228.
  8. Koene, P. (1997). Communication of Scottish highland bulls: context specific and individual specific vocalizations. In proceeding of the XXV International ethological conference. Blackwell: Oxford, PP: 24.
  9. Kramer, C.Y. (1957). Extension of multiple range test to group correlated adjusted means. Biomet., 13:13-18.
  10. Lee, C.H., Chou, C.H., Han, C.C. and Huang, R.Z. (2006). Automatic recognition of animal vocalizations using averaged MFCC and linear dicriminant analysis. Pattern. Recogn. Lett. 27(2): 93-101.
  11. Mielke, A. and Zuberbühler K. (2013). A method for automated individual, species and call type recognition in free-ranging animals. Ani. Behav. 86(2): 475-482.
  12. Singh, Y., Lathwal, S.S., Rajput, N., Raja, T.V., Gupta, A.K., Mohanty, T.K., Ruhil, A.P., Chakravarty, A.K., Sharma, P.C., Sharma, V. and Chandra, G. (2013). Effective and accurate discrimination of individual dairy cattle through acoustic sensing. Appl. Anim. Behav. Sci., 146:11-18.
  13. Watts, J.M., and Stookey, J.M., (2000). Vocal behavior in cattle; the animals commentary on its biological processes and welfare. Appl. Anim. Behav. Sci., 67:15-33.
  14. Yin, S., and McCowan, B. (2004). Barking in domestic dogs; context specificity and individual identificantion. Anim. Behav. 68: 343-    355. 
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Indian Journal of Animal Research
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    Research Article

    volume 53 issue 7 (july 2019) : 864-869,   Doi: 10.18805/ijar.B-3601

    Discrimination of karan fries cow’s individuality by the mean of their vocal acoustic features

    R
    Rohit Gupta
    S
    Surendra S. Lathwal
    P
    Pragya Bhadauria
    S
    Shilpi Kerketta
    A
    Ahmad Fahim
    I
    Indu Devi
    Y
    Y.S. Jadoun
    1Punjab Agriculture University, Ludhiana-141004, Punjab, India.
    Cite article:- Gupta Rohit, Lathwal S. Surendra, Bhadauria Pragya, Kerketta Shilpi, Fahim Ahmad, Devi Indu, Jadoun Y.S. (2018). Discrimination of karan fries cow&rsquo;s individuality by the mean of their vocal acoustic features. Indian Journal of Animal Research. 53(7): 864-869. doi: 10.18805/ijar.B-3601.

    ABSTRACT

    Sound is one of the most important means of conveying information over long distances as well as in close vicinity. Utterance of animals becomes unique for them when they communicate their individuality or physiological state to the other co species partner. Present study was based on hypothesis of discrimination of individual identity through vocal signal of lactating karan fries (KF) cows. For this 25 KF cows were selected for recording of their vocal signal. Vocal call was recorded after separation of animal from their living herd in the morning hours.  Acoustic features of vocalization of individual cow were extracted with the help of PRAAT acoustic analysis software. Analysis of all acoustic features extracted from 250 voice samples of 25 KF cows revealed that differences for amplitude, Total energy, pitch, intensity, formants, pulse, periods, unvoiced frames, voice breaks, jitter, shimmer, mean noise/harmonic ratio and mean harmonic/noise ratio were found highly significant (p < 0.001). Out of these only few acoustic features viz. pulse, pitch, jitter, shimmer, voice break and formants were observed to have significant (p<0.05) difference between each and every individual KF cow. Among these feature formants frequency of every individual cow had a unique pattern in the distribution of frequency contour in their vocal spectrogram. Study concluded that vocal signal of KF cow have some unique feature at individual level, by which cow identification could be made. 

    REFERENCES

    1. Clemins, P.J., Johnson, M.T., Leong, K.M. and Savage, A. (2005). Automatic classification and speaker identification of African elephant (loxodonta Africana) vocalization. J. Acoust. Soc. Am. 17: 23-27
    2. Fant, G. (1960). Acoustic theory of speech production. The Hague: Mouton. PP: 12.
    3. Fitch, W.T. and Hauser, M.D. (1995). Vocal production in non-human primates: acoustics, physiology, and functional con- straints on ‘honest’ advertisement. Am. J. Primatol. 37:191–219.
    4. Harvey, W.B. (1987). Users guide for LSMLMW, Mixed Model Least Squares and Maximum Likelihood Computer Programme. PC-    I version, mimeograph. Ohio State university, OH, USA, PP: 06
    5. Hauser, M. D. (1996). The Evolution of communication. MIT Press, Cambridge, PP: 27
    6. Ikeda, Y. and Ishii, Y. (2008). Recognition of two psychological condition of a single cow by her voice. Compt. and Elect. in Agri., 62:67-72
    7. Ikeda, Y., Jahns, G., Kowalczyk, W. and Walter, K. (2000). Acoustic Analysis to Recognize Individuals and Animal Conditions. In Proceddings of the XIV Memorial CIRG World Congress, PP: 220-228.
    8. Koene, P. (1997). Communication of Scottish highland bulls: context specific and individual specific vocalizations. In proceeding of the XXV International ethological conference. Blackwell: Oxford, PP: 24.
    9. Kramer, C.Y. (1957). Extension of multiple range test to group correlated adjusted means. Biomet., 13:13-18.
    10. Lee, C.H., Chou, C.H., Han, C.C. and Huang, R.Z. (2006). Automatic recognition of animal vocalizations using averaged MFCC and linear dicriminant analysis. Pattern. Recogn. Lett. 27(2): 93-101.
    11. Mielke, A. and Zuberbühler K. (2013). A method for automated individual, species and call type recognition in free-ranging animals. Ani. Behav. 86(2): 475-482.
    12. Singh, Y., Lathwal, S.S., Rajput, N., Raja, T.V., Gupta, A.K., Mohanty, T.K., Ruhil, A.P., Chakravarty, A.K., Sharma, P.C., Sharma, V. and Chandra, G. (2013). Effective and accurate discrimination of individual dairy cattle through acoustic sensing. Appl. Anim. Behav. Sci., 146:11-18.
    13. Watts, J.M., and Stookey, J.M., (2000). Vocal behavior in cattle; the animals commentary on its biological processes and welfare. Appl. Anim. Behav. Sci., 67:15-33.
    14. Yin, S., and McCowan, B. (2004). Barking in domestic dogs; context specificity and individual identificantion. Anim. Behav. 68: 343-    355. 
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