## 1 Introduction  
Sound event localization and detection (SELD) is an audio processing task that unifies the tasks of sound event detection (SED) and its source localization by jointly recognizing target classes of sound events including temporal information of the sound activations and estimating their directions of arrival (DOA) [1, 2, 3, 4]. For example, it can be used in indoor or outdoor situations, such as a baby crying, a window broken, a car horn on the road, and a disaster relief call. In these situations, it is reasonable to combine detection and localization by estimating the temporal and spatial locations of the target events since the sound of an event is transmitted to microphones from the corresponding source in a specific direction. Therefore, SELD has various applications including acoustic monitoring [5], surveillance [6], autonomous driving, and robotics [7], and the task has become such a popular topic that it was introduced into the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) Task3 in 2019 [8]. Since sound sources should be localized, SELD typically requires multi-channel audio data acquired by a microphone array.  
In DCASE2021, the TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset is provided in two four-channel spatial audio formats, the raw signals of a tetrahedral microphone array (MIC) and first-order Ambisonics (FOA), which included emulation of recordings of static or moving sound events by spatial room impulse responses (SRIRs) captured in various rooms [3]. The isolated sound event recordings were obtained from the NIGENS general sound events database [9], and the spatialization of all sound events was based on filtering through spatial room impulse responses. Localized interfering events outside of the target classes and spatial ambient noise captured in these rooms were added to the recordings [3]. In this paper, we only used the FOA (four-channels, three-dimensional recordings) data format. The spatial responses of the four FOA channels to a source incident from DOA given by azimuth angle \(\phi\) and elevation angle \(\theta\) are as follows [10]:  
\[H_{1}(\phi,\theta,f) = 1, \tag{1}\] \[H_{2}(\phi,\theta,f) = \cos(\phi)\cos(\theta),\] (2) \[H_{3}(\phi,\theta,f) = \sin(\phi)\cos(\theta),\] (3) \[H_{4}(\phi,\theta,f) = \sin(\theta). \tag{4}\]  
The FOA format is obtained by converting the 32-channel microphone array signals by means of encoding filters based on anechoic measurements of the Eigenmike array response.  
The task contains 12 different SED target classes of alarm, crying baby, crash, barking dog, female scream, female speech, footsteps, knocking on the door, male scream, male speech, ringing phone, and piano, and directional interferers of running engine and burning fire, and an additional general class with disparate sounds not belonging to any of the other classes that have their own temporal activities from a static or moving source. Up to three overlapping target events may occur, and these events may belong to the same class. The dataset consists of 800 one-minute spatial recordings that are split into the development set of 600 recordings and the evaluation set of 200 recordings. The development set is split into six disjoint sets of 100 recordings each. In total, 500 data (fold 1, 2, 3, 4, 5) were used for model training, and the other 100 (fold 6) were used as the test set to permit evaluation across different acoustic conditions. Note that in this challenge, the use of external data is not allowed, and a pre-trained network cannot be used to develop a model. The dataset can be augmented any number of times under the condition that no external data are used.