Journal Article

Research on sound classification based on SVM

Li, Li, Li, Jing, Wei, Pengcheng, He, Fangcheng

2020

Overview

Sound is a ubiquitous natural phenomenon that contains a wealth of information that constantly enhances our understanding of the objective world. With the continuous development of computer network technology and communication technology, audio information has become a very important part. Audio is a non-semantic symbolic representation and an unstructured binary stream. Because the audio itself lacks the description of content semantics and structured organization, it brings great difficulty to the audio classification work. The research of digital audio classification will become more and more important with the increasing number of digital audio resources in the network. Digital audio classification technology is the key technology to solve this problem. It is the key to solve the problem of audio structure and extract audio structured information and content semantics. It is a research hot spot in the field of audio analysis. It has important application value in many fields, such as audio retrieval, video summary and auxiliary video analysis. This paper studies the structure of audio, the analysis and extraction of audio features, the digital audio classifier based on support vector machines (SVM) and the audio segmentation technology based on BCI. SVM is an important achievement of machine learning research in recent years. As a new machine learning method, SVM can solve practical problems such as small sample, nonlinearity and high dimension, so it has become a new research hot spot after the study of neural network. Experiments show that the SVM-based audio classification algorithm has good classification effect, and the smoothed audio segmentation results are more accurate. With the further development of the research, the research results will be well applied in practice.

• Publisher: Springer London,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Audio data / Classification / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer networks / Computer Science / Data Mining and Knowledge Discovery / Deep Learning for Big Data Analytics / Feature extraction / Image Processing and Computer Vision / Machine learning / Neural networks / Probability and Statistics in Computer Science / Segmentation / Semantics / Support vector machines