Research on Cognitive Model and Interactive Technology of Music Emotion Based on Deep Belief Network

In the realm of information and multimedia advancements, music digitization has become prevalent across diverse media platforms such as radio, digital storage, and networks. Amidst the big data era, extracting valuable insights from seemingly unstructured data holds significant promise for industry growth and economic benefits. The evolution of recommendation technology now enables tailored information delivery to users from vast datasets, offering personalized services. Currently, recommendation techniques predominantly encompass collaborative filtering, model-based approaches, and deep learning-driven algorithms. Computer music, a pivotal multimedia form, has garnered considerable attention for its research and practical applications. Music, as a primal communication medium, is rich in emotional depth, sparking keen interest in computer-aided musical emotion analysis and interactive technologies. The identification of musical features serves as a foundation for building emotional cognitive models and interactive experiences. Research in the emotional cognition and interactivity of music plays a pivotal role in enhancing natural and seamless human-computer interactions, multimedia advancements, and computer music studies. This field aligns with the exploration of artificial emotions and holds immense potential in elevating the emotional responsiveness of digital media and entertainment offerings, thus propelling research in emotional human-computer interaction forward.