The field of audio processing and analysis is rapidly advancing, with a focus on multimodal approaches that combine audio with other modalities such as text, images, and videos. Recent developments have seen the integration of large language models and transformer architectures to improve the accuracy and efficiency of audio processing tasks. One of the key areas of research is the use of multimodal fusion techniques to combine different modalities and improve the robustness of audio processing systems. Another area of focus is the development of more efficient and scalable models, such as the use of state space models and retrieval-augmented generation frameworks. These advances have the potential to improve a wide range of applications, including speech recognition, depression detection, and music generation. Notable papers in this area include the proposal of a novel multimodal framework for depression detection, which combines textual, user-specific, and image analysis to detect depression among social media users. Another notable paper is the introduction of a large audio-language model tailored for multiple Southeast Asian languages, which exhibits strong performance across diverse audio-centric tasks.