###### Abstract  
In this paper, we propose a method for estimating the classes and directions of static audio objects using stereo microphones in a drone environment. Drones are being increasingly used across various fields, with the integration of sensors such as cameras and microphones, broadening their scope of application. Therefore, we suggest a method that attaches stereo microphones to drones for the detection and direction estimation of specific emergency monitoring. Specifically, the proposed neural network is configured to estimate fixed-size audio predictions and employs bipartite matching loss for comparison with actual audio objects. To train the proposed network structure, we built an audio dataset related to speech and drones in an outdoor environment. The proposed technique for identifying and localizing sound events, based on the bipartite matching loss we proposed, works better than those of the other teams in our group. deep learning; sound event localization and detection; convolutional  
is also useful in noisy environments. In places like airports, railway stations, or large factories where background noise is prevalent, drones can recognize specific noise patterns to distinguish between normal operational sounds and those indicative of emergencies or malfunctions. By integrating sound detection capabilities, drones not only enhance visual assessments but also add an auditory dimension to environmental monitoring, making them even more versatile as tools for ensuring public safety and efficient response to critical situations. The advent of deep learning significantly revolutionized sound event detection (SED) research [6, 7]. The introduction of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitated the processing of complex sound patterns, leading to enhanced detection accuracies [8, 9]. This period was also characterized by the development of larger and more diverse datasets, like UrbanSound8K [10] and ESC-50 [11], which allowed for the training of more robust models capable of recognizing a wide range of sound events. Consequently, SED began to find real-world applications in various domains, ranging from smart home systems to urban soundscapes, focusing on detecting specific sound events pertinent to areas like security, healthcare, and environmental monitoring. Recent advancements in SED research have involved the adoption of more complex model architectures, including convolutional recurrent neural networks (CRNNs) and transformers [8, 9, 12]. These architectures effectively combine the spatial feature extraction capabilities of CNNs with the temporal processing strengths of RNNs or the attention mechanisms of transformers. A significant focus of contemporary research is polyphonic SED, where multiple sound events occur simultaneously [8, 9]. Researchers have developed techniques such as multi-label classification and sound source separation to address this challenge. Moreover, the integration of SED with other technologies, such as drones and IoT devices, has broadened its application scope, encompassing areas like emergency response, wildlife monitoring, and industrial inspection. Sound event localization and detection (SELD) is a task that combines sound event detection (SED) and sound source localization (SSL) [13, 14, 15, 16, 17]. SED classifies acoustic events occurring within an audio signal and identifies the start and end points of these classified events. SELD, as a fusion of SED and SSL, not only classifies and detects the activation of sound events within an audio signal but also estimates the location of these activated events. The ability of SELD to discern acoustic events and their locations makes it a valuable tool in scenarios where visual information is absent or inadequate. Therefore, systems equipped with SELD technology are apt for detecting crimes and emergency situations in spaces where individual privacy is paramount. One approach to implementing SELD is to perform SED and SSL independently and then to combine the results from each task.