The field of audio understanding and multimodal learning is witnessing significant developments, with a focus on improving the ability of models to comprehend complex audio scenes and events. Researchers are exploring new benchmarks and evaluation metrics to assess the performance of large audio language models, highlighting the need for more comprehensive and realistic testing scenarios. Furthermore, there is a growing interest in spatial audio understanding, with efforts to develop frameworks that can interpret and reason about auditory scenes, including moving sources and multi-source conditions. The integration of audio and visual signals is also being investigated, with novel approaches to aligning and fusing these modalities to improve recognition and classification performance. Noteworthy papers include:

• A study introducing a new benchmark for evaluating the audio understanding performance of large audio language models, which highlights the importance of considering energy differences between speech and non-speech audio.
• A framework for spatial audio motion understanding and reasoning, which demonstrates the effectiveness of conditioning a large language model on structured spatial attributes extracted from audio signals.
• A novel approach to learning spatially-aware audio-text embeddings, which introduces a content-aware spatial encoder and a spatial contrastive learning strategy to promote more reliable embeddings under multi-source conditions.
• A method for multimodal acoustic event classification, which constructs a temporal graph for each event and uses contrastive learning to capture fine-grained relationships between audio and visual signals.