The field of audio processing and music generation is rapidly evolving, with a focus on developing more robust and accurate methods for detecting AI-generated content, improving speech recognition, and enhancing music generation. Researchers are exploring innovative approaches, such as multimodal fusion and adversarial training, to overcome the limitations of existing methods. Notably, the development of hybrid models that combine audio and lyrics information is showing promising results in detecting AI-generated music. Furthermore, advancements in language-queried audio source separation and automated speaking assessment are enabling more effective evaluation of content relevance and language use. Noteworthy papers include: Double Entendre, which proposes a novel approach to detecting AI-generated lyrics using a multimodal late-fusion pipeline. A Fourier Explanation of AI-music Artifacts, which mathematically proves that AI-generated music exhibits systematic frequency artifacts and proposes a simple detection criterion. ClearerVoice-Studio, an open-source speech processing toolkit that bridges advanced research and practical deployment. Hybrid-Sep, a two-stage language-queried audio source separation framework that synergizes pre-trained self-supervised learning models with Contrastive Language-Audio Pretraining frameworks.