Harmonizing Innovation: Chorus Work Creation and Innovation through Deep Learning and Generative Models

Abstract A precise characterization of the chorus's timbre is essential for accurate identification. It is imperative to provide a detailed representation of the timbre for analyzing temporal frequency domain characteristics, inverted spectrum features, sparse attributes, and ultimately, for chorus identification. Utilizing both temporal and frequency domain attributes enables effective differentiation of the chorus. This is exemplified through the examination of probability features, inverted spectrum characteristics, temporal frequency domain attributes, and inverted spectrum features. To enhance the temporal-frequency representation of the chorus's timbre and facilitate chorus identification, deep learning techniques are applied. The objective of this research is to analyze the structural elements of an intelligent music chorus detection and evaluation system employing deep learning. In response to significant classification errors, this study introduces a noise-reducing instrument identification model that mimics human hearing using a cochlear model with harmonious decomposition of music, incorporating time and frequency information in the auditory spectrum. This model utilizes probability characteristics, time-domain attributes, congestion features, and sparse attributes to evaluate the adverse impacts of instrumental interference. A deep hybrid network, integrating generative deep learning and an LSTM framework, establishes a correlation between the abstract feature extraction capability of the generative deep network and the feature expression capability of the serial-level noise cancellation encoder. The proposed method demonstrates remarkable accuracy on datasets and significantly improves the overall evaluation effectiveness.