Optimization of Vocal Singing Training Methods Based on Multimedia Data Analysis

This paper constructs a vocal singing training optimization model based on multimedia data analysis and conducts in-depth research and analysis on the optimization of vocal singing training methods using multimedia data analysis system. For the problems of poor real-time and large analysis error of the traditional system, a massive real-time multimedia data analysis system based on DSP is designed, with high-speed DSP as the core architecture, multipoint modeling of the experimenter, and data extraction and fusion processing are completed with model association characteristics as the analysis basis. The experimental results show that the real-time performance and accuracy of the system are improved by 15.14% and 13.42%, respectively. To address the time-space domain consistency problem in traditional time series data forecasting, two high-order time series forecasting algorithms based on tensor decomposition are proposed in this paper: the multilinear orthogonal autoregressive model and the multilinear constrained autoregressive model. Using the local structure information of the data to reduce the dimensionality of the data is more beneficial to extract the true low-dimensional representation of the data. Most current supervised cross-modal retrieval methods use a strict binary label matrix (i.e., 0 and 1); however, the interval between labels 0 and 1 is small, which may increase the risk of classification errors. To solve some of the above existing problems, a two-stage hashing method based on smooth matrix decomposition and label relaxation is proposed, which proposes a novel label relaxation strategy that can adaptively control the interval between label intervals and can reduce the quantization loss by about 5.21%. The multimedia data analysis vocal singing training optimization model designed in this paper can improve enhance students’ motivation and accuracy and provide technical support and a rational basis for vocal singing practice and overall musical expression.