Abstract
Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. This method has the advantages of simple structure, mature algorithm, and accurate optimization. It can find better network weights and thresholds so that particles can jump out of the local optimal solutions previously searched and search in a larger space. The global search uses the gradient method to accelerate the optimization and control the real-time generation effect of the music style transfer, thereby improving the learning performance and convergence performance of the entire network, ultimately improving the recognition rate of the entire system, and visualizing the musical perception. This kind of real-time information visualization is an artistic expression form, in which artificial intelligence imitates human synesthesia, and it is also a kind of performance art. Combining traditional music visualization and image style transfer adds specific content expression to music visualization and time sequence expression to image style transfer. This visual effect can help users generate unique and personalized portraits with music; it can also be widely used by artists to express the relationship between music and vision. The simulation results show that the method has better classification performance and has certain practical significance and reference value.
Highlights
Each particle searches for the optimal solution individually in the search space, records it as the current individual extreme value, shares the individual extreme value with other particles in the entire particle swarm, and finds the optimal individual extreme value as the entire particle
Interactive sound visualization emphasizes the process of interactive communication with the audience, so it is necessary to explore the visual representation of sound from the audience’s perspective [5]
Regarding the current global optimal solution of the swarm, all particles in the particle swarm adjust their speed and position according to the current individual extreme value they find and the current global optimal solution shared by the entire particle swarm
Summary
Music visualization combines hearing and vision to enhance the comprehensibility and emotional resonance of music. Interactive sound visualization can play an active role in speech recognition, music performance, and other aspects Matching and applying their respective tags, this theory proposes a system that combines audio and physiology to generate a music visualization system that can respond to music and listeners’ wake-up responses in real-time and provide the music and auditory experience obtained in the sound and physiology fields Artistic visual representation [3]. E above two kinds of music visualization research are based on people labeling music or images, or the unilateral transformation effect of algorithm application, and, to a certain extent, realize the highly subjective creative expression in the field of art but miss the music creation and image shooter. By extracting the essential information of music or image transmission, the subjective label of the creator is no longer added, but the computer is allowed to complete the two links of identification and output of the original music and image files, which can retain the information in the original file to the maximum extent
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