This paper utilizes intelligent technology to assist repertoire teaching and singing instruction, combining with the existing computer music software, to explore the development path of the traditional choral teaching mode in the intelligent era. For the teaching of choral repertoire, a real-time music beat tracking algorithm is proposed, which carries out wavelet transform on the pre-processed music signals, detects the peaks of the resulting detail coefficients, constructs and solves the smooth histogram of the music beats, and obtains the real-time value of the music beats. In addition, in order to improve teachers’ guidance to students’ singing, deep convolutional networks with powerful dimensionality reduction and feature learning ability are used to embed high-dimensional and time-sequential vocal spectral features into the 3-dimensional timbre embedding space, to realize the characterization and similarity metrics of vocal timbre in the 3-dimensional timbre embedding space, and to build a vocal timbre characterization model. The experimental samples are selected and the experimental group and control group are set up, in which the students in the experimental group have 1-6 different semitones of range broadening with the technical assistance of the algorithms and models in this paper, which verifies the important technical roles of real-time music beat tracking algorithms and vocal coloration characterization models in reconstructing the traditional choral teaching mode.
