## 1 Introduction  
People experience a variety of audio events with meaningful information that can be useful for human activities. Audio event detection (AED) aims to identify the occurrence of specific sounds in audio recordings. As the amount of multimedia data on the Internet is growing rapidly, analyzing audio events will help in describing and understanding environmental and social activities in video and audio contents. AED is also useful in many other applications, including surveillance, self-driving cars, healthcare, smart home systems, and military applications.  
In early studies on AED, several approaches were proposed based on signal processing and machine learning techniques, and recently deep learning based methods have been widely developed. Most of these studies are based on fully supervised learning methods that require strongly labeled data. In strongly labeled data, either audio event examples are directly provided or the exact time of each audio event is given. However, building a large strongly labeled database is a time-consuming and challenging work. For these reasons, there exist only a few publicly available large-scale audio event datasets with strong labels.  
Recently, there have been some studies on weakly supervised AED [1; 2; 3; 4]. These studies focus on learning AED models based on weakly labeled data that provide only the presence or absence of events in the recording. We can obtain weakly labeled datasets much more easily than strongly labeled datasets. For example, we can collect videos uploaded on the Internet and use the tags (user-generated keywords which describe the content of videos) of the videos as weak labels. However, it is problematic to directly use these data for AED since the exact occurrence times of the events are not known, which makes it difficult to learn a model for segment-level predictions.  
Most of the AED methods use spectro-temporal representations as input features. Since the spectro-temporal feature of an audio signal, such as log mel spectrogram, can be considered as a 2D image, computer vision techniques can be applied to AED. In recent works on computer vision, deep learning approaches including convolutional neural network (CNN) models such as the residual network (ResNet) [5], the densely connected convolution network (DenseNet) [6], and the squeeze-and-excitation network (SENet) [7] have shown impressive performance. In addition, many studies report that better results are obtained by using structured prediction methods, which consider dependencies of each pixel-level output [8; 9].  
In this paper, we propose a deep convolutional network based on DenseNet and SENet for weakly supervised AED. We take advantage of strengthening feature propagation from DenseNet and modeling channel-wise relationships by SENet. In addition, the correlations among segments in recordings are considered through a recurrent neural network (RNN) and conditional random field (CRF) [10]. We evaluated our proposed method and compared its performance with a CNN-based baseline approach. Empirical results show that the proposed method outperformed the baseline on the DCASE 2017 task 4 dataset.  
The rest of the paper is organized as follows. In Section 2, we introduce previous works on AED and related studies. In Section 3, we present our proposed model for weakly supervised AED. Section 4 describes the experimental settings and performance measurement. In Section 5, we present the experimental results. We draw some conclusions in Section 6.