Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Abstract

Aiming at the complex and changeable characteristics of intelligent singing skills in the context of Internet of Things, this paper proposes a feature extraction method suitable for intelligent singing skills in this context. Firstly, focusing on vocal features, the time-domain algorithm based on open-loop and closed-loop gene extraction extracts the genetic features of songs with accompaniment; then, the section and its features are extracted by using the windowed moving matching algorithm, and the segments are divided by using the similarity between adjacent segments to obtain the segment features with emotional factors. The segment features are input into the improved BP emotion recognizer for emotion recognition. Finally, the intelligent singing skills of the whole music are determined. The experimental results show that, with the increase in feature extraction time, the accuracy of the extraction results of the existing methods changes little, which is basically maintained at a low level between 15% and 30%. When the proposed method is for feature extraction of intelligent singing skill information, the accuracy shows a continuous growth trend, and with the growth of time, its accuracy is significantly higher than the existing methods, indicating that the proposed method has significant advantages in the accuracy of feature extraction. Because this waveform feature extraction method is applied to the intelligent singing skills under the background of the Internet of Things, it has the advantages of high extraction efficiency, high accuracy, and reliability.