The field of speech and music processing is moving towards more sophisticated and innovative methods for improving human-computer interaction, speech restoration, and music performance analysis. Researchers are exploring the integration of nonlinear acoustic computing and reinforcement learning to enhance human-robot interaction, and developing novel frameworks for real-time speech processing and music tracking. Additionally, there is a growing interest in using machine learning and signal processing techniques to improve speech restoration, noise reduction, and music information retrieval. These advancements have the potential to revolutionize various applications, including voice assistants, music education, and speech therapy. Noteworthy papers include: Miipher-2, which introduces a universal speech restoration model for million-hour scale data restoration, and ReverbMiipher, which proposes a generative speech restoration model that preserves and enables control over reverberation characteristics. Pairing Real-Time Piano Transcription with Symbol-level Tracking is also notable, as it presents a novel approach to precise and robust score following.