The field of audio and speech processing is rapidly advancing, with a focus on improving the capabilities of large language models (LLMs) in various tasks such as speech recognition, synthesis, and comprehension. Recent developments have explored the use of test-time compute methods to enhance the auditory cognitive capabilities of Audio LLMs, allowing them to better understand and process audio inputs in real-world environments. Furthermore, researchers have been working on improving the robustness and efficiency of speech separation models, particularly in real-time streaming applications. Another area of research has been focused on protecting user privacy against unauthorized use of personal audio data, with the development of novel frameworks and techniques to safeguard against potential eavesdroppers. Notably, the introduction of new datasets and benchmarks has enabled more accurate evaluation of audio perception capabilities in Music-QA tasks and multimodal reasoning tasks. Overall, these advancements have the potential to significantly improve the performance and applicability of audio and speech processing systems in various domains. Noteworthy papers include: Protecting Your Video Content, which proposes a video watermarking method to protect personal video content from unauthorized use. Scaling Auditory Cognition via Test-Time Compute in Audio Language Models, which explores the use of test-time compute methods to enhance auditory cognitive capabilities. UniSep, which proposes a universal target audio separation model that can handle arbitrary mixtures of different types of audio.