# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 1 Introduction  
Machine learning has experienced a strong growth in recent years, due to increased dataset sizes and computational power, and to advances in deep learning methods that can learn to make predictions in extremely nonlinear problem settings [1]. However, a large amount of data is needed in order to train a neural network that can achieve a good quality performance. With the increased amount of audio datasets publicly available, there is also an increase of tagging labels available for them. We refer to these tagging labels, which only indicate the presence or not of a type of event in a recording and lack any temporal information about it, as weak labels. A lot of research has been done in tagging of audio recordings. In [2], the authors proposed a content-based automatic music tagging algorithm using deep convolutional neural networks. In [3], the authors proposed to use a shrinking deep neural network incorporating unsupervised feature learning to handle the multi-label audio tagging. Furthermore, considering that only chunk level rather than frame-level labels are available, a large set of contextual frames of the chunk were fed into the network to perform this task. In [4; 5], the authors use a stacked convolutional recurrent network to perform environmental audio tagging and tag the presence of birdsong, respectively. However, in [6], the authors explore two different models for end-to-end music audio tagging when there is a large amount of training data. However, in recent decades, there has also been an increase in the demand for transcription predictions for a variety of audio recordings instead of just the tags of a recording. Transcription of audio recordings refers to audio event detection, which provides a list of audio events active in a recording along with temporal information about each of them, i.e., starting time and duration for each event [7; 8; 9; 10]. Some potential applications where audio event transcription is necessary arecontext awareness for cars, smartphones, etc., surveillance for dangerous events and crimes, analysis and monitoring of biodiversity, recognition of noise sources and machine faults and many more. Depending on the audio event to be detected and classified in each task, it may become difficult to collect enough samples for them. Furthermore, different tasks use task specific datasets, hence the amount of recordings available may be limited. Annotating data with strong labels, labels that contain temporal information about the events, to train transcription predictors is a time-consuming process involving a lot of manual labour. On the other hand, collecting weakly labelled data takes much less time, since the annotator only has to mark the active sound event classes and not their exact boundaries. We refer to datasets that only have these types of weak labels, may contain rare events and have limited amounts of training data as low-resource datasets. In comparison to supervised techniques that are trained on strong labels, there has been relatively little work on learning to perform audio event transcription using weakly labelled data. In [11, 12], the authors try to exploit weak labels in birdsong detection and bird species classification, while, in [13], the authors use deep networks to tag the location of bird vocalisations. In [14], singing voice is pinpointed from weakly labelled examples. In [15], the authors used a joint detection-classification network that slices the audio into blocks and an audio detector and classification on each block then uses the overall audio tag to train using the weak labels of a recording. In [10], the authors train a network that can do automatic scene transcription from weak labels and, in [16], audio from YouTube videos is used in order to train and compare different previously proposed convolutional neural network architectures for audio event detection and classification. Finally, in [17, 18], the authors use weakly labelled data for audio event detection in order to move from the weak labels space to strong labels.