Pengenalan Suara Burung Menggunakan Mel Frequency Cepstrum Coefficient dan Jaringan Syaraf Tiruan pada Sistem Pengusir Hama Burung

Indonesia is one of the agricultural country that produces crops. Nevertheless, Indonesia still imports rice from other countries because of crop decreasement. One of its factors is caused by bird pests. In the ricefields ecosystem, the variety of bird species can be classified as a pest and as a non-pest. Non-pest birds usually help farmer against insects. Farmers are using traditional methods to repel bird pests. In this research, a software to recognize species of birds has been designed. The system is based on birdchirp types. A mono-microphone is used to capture the sound. Voice Activity Detection (VAD) method is used for birdchirp detection. Mel Frequency Cepstrum Coefficient (MFCC) and Fast Fourier Transform (FFT) are used to extract birdchirp feature. Artificial Neural Network is utilized to recognize the pattern of birdchirp feature. Furthermore, audiosonic bird repeller is used to repel bird pests. In the offline mode testing, success level using MFCC feature extraction is up to 90% for birdchirp variation, while up to 68% using FFT. In the online mode, the average success level using MFCC feature extraction is 70% for finch birds, while 30% using FFT. In addition, a gunshot is a best sound to repel bird pests. The success rate of bird voice recognitions using MFCC feature extraction is higher than that using FFT