Shahriza Shahrudin

MFCC based frog identification system in noisy environment

Identification of frog sound is useful tool and competent in biological research and environmental monitoring. In contrast with traditional methods that not practical due to the time consuming, expensive or detrimental to the animals welfare, this study proposes an automatic frog call identification system. 750 data species that recorded from Malaysia forest is used as data signals and have been corrupted by 10dB and 20dB noise to determine the performance of accuracy in noisy environment. MFCC parameter is employed as feature extraction. An analysis of signals for different number of MFCCs (8, 12, 15, 20 and 25) is presented and the results are provided using MFCC, Delta Coefficients (ΔMFCC) and Delta Delta Coefficients (ΔΔMFCC). Subsequently, kNN classifier is applied to evaluate the performance in the frog identification system. The results show the accuracy range from 84.67% to 85.78%, 61.33% to 68.89% and 59.33% to 67.33% in clean environment, 10dB and 20dB, respectively.

Frog Identification System Based on Local Means K-Nearest Neighbors with Fuzzy Distance Weighting

Frog identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed. Yet, the k-nearest neighbor (kNN) is one of the popular classifiers and has been applied in various applications. This paper proposes an improvement of kNN in order to evaluate the accuracy of frog sound identification. The recorded sounds of 12 frog species obtained in Malaysia forest have been segmented using short time energy and short time average zero crossing rate while the features are extracted by mel frequency cepstrum coefficient. Finally, a proposed classifier based on local means kNN and fuzzy distance weighting have been employed to identify the frog species. Comparison of the system performances based on kNN, local means kNN and the proposed classifier i.e. fuzzy kNN with manual segmentation and automatic segmentation is evaluated. The results show the proposed classifier outperforms the baseline classifier with accuracy of 94.67 % and 98.33 % for manual and automatic segmentation, respectively.