Research on Music Soundtrack Recognition Based on Deep Computer Learning

In an era where information technology is increasingly prevalent, music score recognition, as a vital branch of intelligent audio processing, has garnered significant attention. It not only enhances the listening experience for users but also holds substantial importance in realms such as music copyright protection, automatic scoring, and intelligent editing. The advent of deep learning technologies in computing resembles a revolution, transforming traditional audio analysis methods and rendering the processing of complex audio data more efficient and precise. By leveraging advanced deep learning models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Belief Networks (DBN), researchers can extract valuable insights from vast troves of musical data, gradually uncovering the underlying patterns hidden within notes and melodies. Particularly when confronted with an array of diverse forms and styles in musical compositions, deep learning techniques significantly enhance the accuracy and speed of score recognition, revealing extraordinary potential in applications such as automatic tagging, recommendation systems, and real-time audio processing.