The fields of acoustic signal processing, spatial audio, audio signal processing, underwater image and video enhancement, and audio and image processing are experiencing significant advancements. A common theme among these areas is the use of deep learning techniques to improve the accuracy and efficiency of various methods. Notable papers include Latent Acoustic Mapping for Direction of Arrival Estimation, SonicMotion, and VP-SelDoA, which introduce innovative approaches to sound source localization and immersive spatial audio. In audio signal processing, researchers are exploring new methods for beamforming and multichannel audio processing, such as Adaptive Linearly Constrained Minimum Variance Volumetric Active Noise Control and Beamforming with Random Projections. The field of underwater image and video enhancement is also rapidly advancing, with a focus on incorporating human perception and subjective image quality into the enhancement process. Papers like Enhancing Underwater Images Using Deep Learning with Subjective Image Quality Integration and Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement showcase the potential of deep learning-based methods. Additionally, the development of specialized tracking frameworks and benchmarks for underwater multiple fish tracking has important applications in marine ecology and aquaculture. The field of audio and image processing is evolving towards more efficient transmission, compression, and analysis of multimedia data, with noteworthy papers including the introduction of the IMPACT model and the development of the LISTEN model. These advances have significant implications for a wide range of applications, including audio question answering, image compression, and industrial monitoring. Overall, the field is poised for continued innovation and growth, with a focus on developing more efficient, accurate, and robust models for analyzing and understanding multimodal data.