The field of audio and visual processing is moving towards more robust and efficient methods for tasks such as music analysis, audio fingerprinting, and person re-identification. Recent developments have focused on improving the accuracy and scalability of these methods, with a particular emphasis on leveraging pre-trained models and novel architectures to achieve state-of-the-art performance. Notable papers in this area include: Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders, which demonstrates the emergence of a perceptual hierarchy in music representations. Shared Latent Representation for Joint Text-to-Audio-Visual Synthesis, which proposes a framework for text-to-talking-face synthesis leveraging latent speech representations. Robust Neural Audio Fingerprinting using Music Foundation Models, which develops new neural audio fingerprinting techniques using pre-trained music foundation models. ReIDMamba: Learning Discriminative Features with Visual State Space Model for Person Re-Identification, which proposes a pure Mamba-based person ReID framework. LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification, which integrates a CNN backbone and a Transformer backbone to extract both local and global features from latent fingerprints. HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios, which proposes an efficient framework for high-quality zero-shot SVC.