###### Abstract
This method can involve a blend of classical algorithms or the application of deep learning models. However, when combining the outcomes of these tasks, a tracking challenge emerges, especially when multiple overlapping events occur simultaneously [18]. This challenge necessitates the correct linkage of the results from each task to their corresponding events. To circumvent this issue, methods that concurrently perform SED and SSL have been developed. These methods can also employ traditional techniques or be based on deep learning algorithms. The advancement and improvement of deep learning algorithms have spurred various research endeavors in the field of deep learning-based SELD, offering potential solutions to previously challenging problems [19]. The end-to-end detection transformer (DETR) represents an innovative paradigm in object detection, predominantly utilized for identifying the positions and classes of objects within images [20]. Distinct from traditional object detection methodologies, DETR circumvents the necessity for anchor boxes and complex post-processing stages [21]. The framework is grounded in the transformer architecture, initially employing a CNN to extract features from images. These features are subsequently fed into the transformer, which concurrently predicts information regarding the object's location and class. This process incorporates the Hungarian algorithm for bipartite matching, culminating in the final prediction [22]. DETR offers a simplistic yet efficient alternative to conventional,intricate object detection systems, demonstrating exceptional performance, particularly in scenarios involving large-scale objects or scenes. In this paper, we present a novel neural network architecture for static sound event localization and detection, inspired by the principles of detection transformer (DETR) applied in the auditory domain. Specifically, our method is the use of the ResNet-50 model within a CNN framework [23], tailored to process audio signals segmented into 30-second intervals. Unlike traditional object detection methods in computer vision, which rely on numerous grids and anchors, leading to redundant detections refined through techniques like non-maximum suppression (NMS), our approach is anchor- and NMS-free. It infers a fixed-size set of audio predictions, exceeding the actual count of audio objects in the mel-spectrogram, ensuring all audio objects are captured without relying on image-based post-processing methods. This approach is further augmented by bipartite matching using the Hungarian algorithm, aligning the order of audio predictions with the actual audio objects and efficiently handling the classification, localization, and detection (onset and offset) of audio events. This innovative methodology provides a bespoke solution for sound event localization and detection, leveraging advancements in object detection and adapting them creatively to the auditory context. The structure of our paper is organized as follows: Following the introduction, Section 2 delves into the methodology of sound event localization and detection based on bipartite matching loss. In Section 3, we discuss the experiments conducted to evaluate the performance of our proposed method. Finally, Section 4 concludes the paper, summarizing our findings and contributions to the field of sound event localization and detection.