## 2 Related Work  
Early works on AED focus on detecting audio events based on various machine learning techniques. Several approaches are proposed based on hidden Markov models (HMMs) [11; 12]. In [12], Gaussian mixture model (GMM)-HMM-based modeling, similar to speech recognition techniques, is proposed to model audio events. Support vector machine (SVM) [13; 14; 15] and non-negative matrix factorization (NMF) [16; 17; 18] are also applied to AED in some studies. Bag of words representation is used to represent and detect audio events with various classifiers [19; 20]. In [21], the use of multi label deep neural networks (DNNs) are proposed for detection of temporally overlapping audio events in realistic environments. Many works on AED have been proposed based on CNN [22; 23; 24]. RNNs have also been utilized in conjunction with DNN or CNN [25]. However, increasing the size of a fully supervised deep learning model is difficult due to a lack of large-scale strongly labeled datasets. This limitation can be somewhat alleviated by model regularization and data augmentation, but it is difficult to overcome the limitation completely. There have been several studies on analyzing and detecting audio events in a weakly supervised scenario. Weakly supervised AED has been widely studied after the release of AudioSet [26], which contains more than two million 10-s YouTube clips with weak audio labels. In the early studies of weakly supervised AED, a multiple instance learning (MIL) [27] based approach is proposed in [1]. The authors formulated weakly supervised AED as a MIL problem and proposed MIL methods based on SVM and DNN. Although the training was done using weakly labeled data without temporal information, the authors showed that temporal localization of audio events was able to be extracted. In [2], the authors proposed a unified framework for supervised and weakly supervised learning (SWSL) using a graph-based model. The proposed model was able to be learned simultaneously from strongly and weakly labeled data. Deep learning based methods have been widely proposed for weakly supervised AED and many of these methods have employed CNNs [3; 4; 28; 29]. In [3], CNN is applied with an event-specific Gaussian filter layer, which is designed to improve its learning ability. [PERSON] et al. [4] proposed a CNN structure with adaptive pooling operators to aggregate temporally dynamic predictions. [PERSON] and [PERSON] [28] used CNN to scan and produce outputs at small segments and then mapped these segment-level outputs to full recording level outputs. [PERSON] et al. [29] used transfer learning to effectively convey knowledge from weakly labeled web audio data to the target data. In the DCASE 2017 [30], most of the top performing methods on the weakly labeled task relied on CNNs [31; 32; 33]. Recent improvements in computer hardware have enabled training very deep CNNs. However, this is not easy due to the problem of vanishing/exploding gradients particularly in lower layers. Many algorithms have been proposed to solve this problem such as ResNet [5]. ResNet introduces a residual block that sums a non-linear transformation of the input and its identity mapping. The identity mapping is implemented through a shortcut connection, which makes the networks avoid the vanishing gradient problem. The shortcut connections help to improve the performance of the networks and obtain faster convergence of training. As an extension of ResNets, a new CNN architecture, called DenseNet, is introduced in [6]. DenseNet is built from stacks of dense blocks and pooling operations. The dense blocks consist of multiple layers with direct connections from any layer to all subsequent layers to improve the information flow between layers. In [7], the authors focused on the channel relationship and proposed a novel architectural unit, the squeeze-and-excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels.