Audio event detection(AED) is one of the main tasks of audio content analysis and processing. The goal is to determine the type of event that occurs in the audio segment and mark the start and end time of the audio event. In recent years, AED has become an important research topic in the field of auditory perception, AED has broad application in security monitoring, medical application, multimedia retrieval, smart home, etc. But there are still many challenges in practical applications. First, in abnormal sound detection(ASD), abnormal data is scarce and difficult to obtain. Second, In real environment scenes, there will be many noises that are difficult to eliminate, and there will be overlapping event sound sources, which will affect the effect of the audio event detection system. Third, since a large amount of strongly labeled data is difficult to obtain, audio event detection on weakly labeled data sets (without timestamps) is particularly important in practical applications. We solve these challenges from the following aspects: firstly, for weakly labeled data, that is, incomplete, fuzzy or wrong labeled data, we carry out weakly supervised learning and detection. The methods include active learning, semi-supervised learning, multi-instance learning, noisy learning and etc. Secondly, for a large number of unlabeled data, because the cost of manual labeling is too high, we use unsupervised learning methods to detect, such as clustering algorithm. Thirdly, in the case of very few or even one audio data to be trained, we try to use one-shot learning or zero-shot learning methods to solve these problems.