Pattern Recognition Bird Sounds Based on Their Type Using Discreate Cosine Transform (DCT) and Gaussian Methods
Corresponding Author(s) : Hendro Nugroho
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 4, No 3, August 2019
Abstract
To know the type of bird, most people know from the shape of bird species and the sound of birds. In this study, it identified the pattern of bird sounds. The bird sounds studied were Canary Trills, Vulture and Crow birds. In the introduction of the type of bird sound pattern in this study using the Discrete Cosine Transform (DCT) method and Gaussian value. The researcher conducted several steps to get the sound model of birds, among others, namely (1) bird sound input in the form of WAV file, (2) Hamming Windowing, (3) DFT / FFT, (4) Mel Bank Filter, (5) DCT, and (6) Value Gaussian. The output obtained is in the form of vector values and represented in graphical form. The results obtained in the study of pattern recognition of bird sound types get the results of observations in the same bird sound duration and frequency of the same, then the same pattern is obtained in the same bird as evidenced by calculating the closest distance value with Bray Curtis method. For the same duration of time and the length of the frequency that is not the same; it found that the pattern of bird sounds is not the same.
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- B. C. K. Dong-III Kim, “Speech Recognition using Hidden Markov Models in Embedded Platform,” Indian J. Sci. Technol., Vol. 8, No. 34, 2015.
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- X. Cheng and Q. Duan, “Speech Emotion Recognition Using Gaussian Mixture Model,” pp. 1222–1225, 2012.
- Suherdiansyah Fajar, “Klasifikasi Gerak Bibir Berdasarkan Pola Suara Menggunakan Metode Mel-Frequency Cepstrum Coefficients (MFCC) dan Hidden Markov Model (HMM) untuk Mengenal Kata Sederhana Indonesia,” Sekolah Tinggi Teknik Surabaya, 2019.
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- A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, “Experiments of Distance Measurements in a Foliage Plant Retrieval System,” Int. J. Signal Process. Image Process. Pattern Recognit., Vol. 5, No. 2, Pp. 47–60, 2012.
References
B. C. K. Dong-III Kim, “Speech Recognition using Hidden Markov Models in Embedded Platform,” Indian J. Sci. Technol., Vol. 8, No. 34, 2015.
S. Ananthi and P. Dhanalakshmi, “Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired,” Int. J. Comput. Appl., Vol. 73, No. 20, Pp. 30–34, 2013.
B. N. Anjali Bala, Kumar Abhijeet Kumar, “Voice command recognition system based on MFCC and VQ algorithms,” World Acad. Sci. Eng. …, Vol. 2, No. 3491, Pp. 501–505, 2009.
S. Berhaningtyas Hertiana, Muh Khaerul Amri S.P, “Pengenalan Huruf Hijayyah Berbasis Pengolahan Sinyal Suara dengan Metode Mel Cepstrum Frequency Cepstrum Coefficient (MFCC),” Momentum, Vol. 13, No. 2, Pp. 49–52, 2017.
Y. R. Prayogi and J. L. Buliali, “Identifikasi parameter optimal,” Vol. 13, Pp. 198–206, 2015.
M. Vyas, “A Gaussian Mixture Model Based Speech Recognition System Using Matlab,” Signal Image Process. An Int. J., Vol. 4, No. 4, Pp. 109–118, 2013.
D. K. Putra, I. Iwut, and R. D. Atmaja, “Simulasi Dan Analisis Speaker Recognition Menggunakan Metode Mel Frequency Cepstrum Coefficient (mfcc) Dan Gaussian Mixture Model (gmm),” eProceedings Eng., Vol. 4, No. 2, Pp. 1766–1772, 2017.
P. Upadhyaya, O. Farooq, M. R. Abidi, and P. Varshney, “Comparative Study of Visual Feature for Bimodal Hindi Speech Recognition,” Arch. Acoust., Vol. 40, No. 4, Pp. 609–619, 2015.
X. Cheng and Q. Duan, “Speech Emotion Recognition Using Gaussian Mixture Model,” pp. 1222–1225, 2012.
Suherdiansyah Fajar, “Klasifikasi Gerak Bibir Berdasarkan Pola Suara Menggunakan Metode Mel-Frequency Cepstrum Coefficients (MFCC) dan Hidden Markov Model (HMM) untuk Mengenal Kata Sederhana Indonesia,” Sekolah Tinggi Teknik Surabaya, 2019.
M. I. Ribeiro, “Gaussian probability density functions: Properties and error characterization,” Inst. Super. Tcnico, Lisboa, Port. Tech. Rep, no. February, Pp. 1049–1, 2004.
A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, “Experiments of Distance Measurements in a Foliage Plant Retrieval System,” Int. J. Signal Process. Image Process. Pattern Recognit., Vol. 5, No. 2, Pp. 47–60, 2012.