The field of audio-based machine learning is rapidly advancing, with a focus on developing innovative solutions for real-world applications. Recent research has explored the use of few-shot learning, meta-learning, and multimodal fusion techniques to improve the accuracy and robustness of audio classification models. These approaches have shown promising results in various domains, including speech emotion recognition, audio event detection, and medical diagnosis. Notably, the integration of audio and visual features has been found to enhance performance in tasks such as micro-expression analysis and audiovisual emotion recognition. Furthermore, the development of parameter-efficient adaptation methods has enabled the effective transfer of knowledge from pre-trained models to new tasks and datasets. Overall, the field is moving towards the development of more robust, scalable, and adaptable audio-based machine learning models that can be applied in a wide range of real-world scenarios. Noteworthy papers include: Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis, which achieved 99% fault diagnosis accuracy with only 1% of labeled sample data. AI-enabled tuberculosis screening in a high-burden setting using cough sound analysis and speech foundation models, which demonstrated strong potential as a TB triage tool with 92.1% accuracy. DyKen-Hyena: Dynamic Kernel Generation via Cross-Modal Attention for Multimodal Intent Recognition, which achieved state-of-the-art results on the MIntRec and MIntRec2.0 benchmarks with a +10.46% F1-score improvement in out-of-scope detection.