This study proposes to classify cricket calls using feature selection and ensemble learning in noisy environments. After collecting cricket calls, we first extract both temporal and frequency features from each frame. Then, statistical features over all frames are calculated including mean, variance, skewness, and kurtosis. For temporal feature, we use zero crossing rate, short-time energy and Shannon entropy. Frequency features include Mel-frequency Cepstral coefficients, spectral centroid, spectral entropy, spectral flux, and spectral roll-off. Next, minimum redundancy maximum relevance is used to select important features and remove redundant information. Finally, ensemble learning of four standard classifiers is used to classify cricket call species and types in noisy environments: k-nearest neighbor, logistic regression, Gaussian naïve Bayes, and random forest. Experimental result shows that the best classification F1-score is 89.5% for classifying five cricket species and two cricket types.