The reporting on nocturnal sounds like cough and snore is not only relevant to follow the progress of respiratory diseases of patients but also to assess the quality of sleep of subjects. In this study, we discuss an audio analysis approach to count individual cough events and the duration of snore sounds in presence of air-conditioner noise through recordings of a smartphone and computationally efficient classifiers. A new audio data set of cough and snore sounds was acquired from 26 subjects. Energy threshold-based segmentation was applied to identify cough or snore events in the original low noise dataset. A k-nearest neighbor classifier was trained to merge cough phases belonging to the same cough event, to derive the proper ground-truth labeling. The original audio signal was augmented by the superposition of air-conditioner noise, with a signal-to-noise ratio of-40dB to 40dB, to enrich the training set of the binary classifier. Nine out of 40 mel-frequency cepstral coefficients in combination with the logarithm of energy from an entire cough or snore event were computed. Various classifiers, such as k-nearest neighbor (k-NN), rule-based classifier, decision tree, random forest, naive Bayes, and support vector machine were benchmarked against each other. The k-NN classifier with k=1 resulted in the highest F-1 scores of.85 and.88 in the binary classification task using generalized and personalized models, respectively, considering noise augmented samples. These results underline the potential of smartphones to objectively report on patient symptoms through audio recordings at night.