Coughing is a cardinal symptom of pulmonary and respiratory diseases, such as asthma, tuberculosis, chronic obstructive pulmonary disease, as well as coronavirus disease (COVID-19). The cough type, strength and frequency are indicators of the disease progression. Thus, several studies focused on the quantitative reporting of coughs through recording by a smart-phone and a sound classifier to provide a cough diary for a patient. However, those approaches report any cough, even coughs which are not caused by the patient. Thus, in this study, we aim to not only detect cough episodes, but also cough events and account coughs produced by the particular patient only. Accordingly, we report on an end-to-end solution for a patient cough diary consisting of three convolutional neural networks. The first recognizes respiratory sounds, including coughing by multi-class classification. The second validates if the cough was produced by the patient. It is based on a Siamese network using triplet-loss during training. Finally, individual cough events are detected by a cough onset classifier. For these three recognition models, we achieved an accuracy of 94%, 74%, and 94%, respectively. Furthermore, we explored the human-level performance of cough source validation through a field experiment involving 10 subjects. Our source validation model slightly outperformed the human cohort in the cough memorization task.