The field of audio processing is witnessing significant advancements with the integration of multimodal approaches, enabling more effective and efficient processing of audio data. Recent developments have focused on improving the representation and alignment of audio and text embeddings, leading to enhanced performance in tasks such as speech synthesis, music evaluation, and audio-visual understanding. Innovative architectures and techniques, including cross-modal representation learning, dual-resolution speech representations, and contrastive alignment, are being explored to push the boundaries of audio processing capabilities. Notably, the introduction of discrete audio tokens has opened up new avenues for efficient and compact representation of audio data, while the application of Fractional Fourier Transform has shown promising results in sound synthesis. Overall, the field is moving towards more sophisticated and multimodal approaches to audio processing, with a focus on improving the accuracy, efficiency, and creativity of audio-related tasks. Noteworthy papers include: WhisQ, which achieved substantial improvements in Mean Opinion Score prediction for text-to-music systems through cross-modal representation learning. Step-Audio-AQAA, which introduced a fully end-to-end large audio language model for Audio Query-Audio Answer tasks, demonstrating exceptional performance in speech control. Fractional Fourier Sound Synthesis, which explored the application of the Fractional Fourier Transform in sound synthesis, showcasing its potential for creating innovative sonic results.