Passive acoustic monitoring has become an effective and economically scalable solution for wildlife monitoring in recent years, especially for population monitoring of endangered birds in hardly accessible and high-elevation areas. As sound recorders are deployed on the field for extended periods (months), continuous sound streams are often complex in nature, with noises and intermittent signals. Generally, it is prohibitively expensive to label bird sounds with the exact onset and offset time, thus, during training, the data is often provided with only presence/absence labeling (weak labeling) that states which bird species are present in each long recording without temporal information. During test time, it is, however, desirable to provide fine-grained detection in short audio segments. To bridge the gap between the difference of weak labels used for training and strong labels used for testing, we propose a re-labeling approach with two stages: (1) we train a model with weak labeling; and (2) using the model obtained from stage 1, we generate labels for short audio segments and retrain the model on short audio segments with the newly generated labels. We applied our approach in both classification and sound event detection and achieved consistently good performance across multiple random seeds. In BirdCLEF 2022, our model ranked in the top 1.1%, the 9th best entry out of the total 807 entries in the competition.