## 1 Introduction
### Existing Methods  
Although SED and sound source localization (SSL) have been independently studied for a long time, research on SELD that performs two tasks simultaneously has been actively conducted only recently, and it was first introduced to a DCASE Challenge task in 2019. Every year, the data handled by this task are updated with difficulty, and the problem to be solved is complicated. In 2020, some sound sources sometimes move. In 2021, up to three overlapping events will occur, and there may be directional noise interference rather than target events. Since not all directional sounds are target events, it is important to match the class and direction of a sound event that occurs. During and after the competition every year, various modeling, features extraction, data augmentation, or other methodologies were introduced by many participants and researchers. In 2019, [PERSON] et al. trained the SED and DOA estimation (DOAE) models separately, and they obtained the DOAE output only when the SED output predicted an event [11]. In 2020, they presented the Event Independent Network (EIN) that outputs track-wise predictions, which enables predicting events of the same class that occur at the same time. When extracting the features of SED and DOAE through the convolutional block, related features between the two tasks were well extracted by applying parameter sharing between the two embeddings [12]. After the challenge, an improved version of the EIN (EINV2) was released, which utilizes multi-head self-attention instead of bidirectional gated recurrent units (biGRUs) after the convolutional layers [13]. In 2020, [PERSON] et al. expressed the labels of SED and SSL as one using the activity-coupled Cartesian DOA vector (ACCDOA) representation [14]. Thus, they did not divide the task into two different ones and considered it as a single system that requires just one loss calculation to optimize their model. They proposed the network called RD3 Net, which uses a dense block with skip connections and dilated convolutions. The ACCDOA vectors are generated as model results, and training is conducted in a direction that minimizesthe distance between the coordinates indicated by this vector and the target coordinates. Therefore, they treated it as a multi-output regression problem rather than a classification problem and used a mean square error (MSE) loss. In 2021, a model was designed in a way that ensembles the ACCDOA-based system and EINV2-based system, and it achieved better performance than all the other methods submitted to the DCASE2021 Task3 [15]. [PERSON] et al. used the SELDNet to jointly estimate polyphonic events of various classes and their source directions [1]. The multi-channel phase and magnitude spectrograms were used as the feature. Multiple layers of two-dimensional (2D) convolutional neural network (CNN) and GRU were followed by two branches of fully connected (FC) layers in parallel for SED and DOAE predictions. The SED and DOAE were performed as a multi-label classification task and a multi-output regression task, respectively. In particular, the modified version of this network was used as the reference system for Task3 in DCASE2019, DCASE2020, and DCASE2021 [3, 10, 16]. [PERSON] et al. proposed a feature named spatial cue-augmented log-spectrogram (SALSA) which stacks multi-channel log-spectrograms with estimated direct-to-reverberant ratio (DRR) and the normalized principal eigenvectors of the spatial covariance matrix at each time-frequency bin on the spectrograms [2, 17]. The normalized eigenvector for FOA corresponds to inter-channel intensity difference (IID) for the FOA format, and this feature outperformed the method based on intensity vector (IV). The ResNet22 network was fixed for the encoder, whereas three different networks were used for the decoder: long short-term memory (LSTM), GRU, and multi-head self-attention. In 2020, [PERSON] et al. designed three kinds of loss functions to address the issue of data imbalance problems [18].