Bird Sound Quality Analysis for Chirping Masters Using Mel Frequency Cepstrum Coefficiens (MFCC) and Svm Classification Algorithm

Zahron, Almeranda Haryaveda Nurul
Kurniawan, Rakhmat

Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Vol 6, No 2 (2025)

Birds play an important role in ecosystems as indicators of environmental health and biodiversity. In Indonesia, there are approximately 1,531 bird species, including songbirds that are popular for their melodious chirps. Bird sounds are used for communication, territorial marking, and are a key attraction in bird song competitions. However, obtaining a bird with high-quality vocalization requires specific training, one of which is the mastering method using recordings of champion bird songs. Additionally, the Support Vector Machine (SVM) algorithm has proven effective in classifying bird species based on sound, achieving 77% accuracy after noise reduction. The combination of MFCC and SVM allows for more systematic and accurate analysis of bird vocalizations. This research is expected to contribute to the field of ornithology, the development of songbird husbandry techniques, and serve as a guide for bird enthusiasts in selecting high-quality master bird sounds.