With the emergence of advanced machine learning techniques and large-scale datasets, holistic analysis of realistic soundscapes becomes more and more appealing. In the case of everyday soundscapes this can mean not only recognizing what are the sounds present in an acoustic scene, but also where they are located and when they occur. This talk will discuss the task of joint detection and localization of sound events addressing the above problem. The state of the art methods typically use spectral representations and deep neural networks based on convolutional, recurrent, and attention layers that share many similarities to neighboring fields. However, the task also has several unique challenges, which will require specific solutions. We will give an overview of the task setup for training machine learning models, acoustic features for representing multichannel signals, topologies of deep neural networks, and loss functions for training systems. Since the performance of the methods is heavily based on the training data used, we will also discuss datasets that can be used for the development of methods and their preparation. We will discuss the recent DCASE evaluation campaign tasks that addressed the problem of joint detection and localization of sound events. Speaker(s): Dr. Tuomas Virtanen, Virtual: https://events.vtools.ieee.org/m/424604