학술논문
Reducing Dimensionality of Spectro-Temporal Data by Independent Component Analysis
Document Type
Conference
Author
Source
2020 2nd International Conference on Computer Communication and the Internet (ICCCI) Computer Communication and the Internet (ICCCI), 2020 2nd International Conference on. :93-97 Jun, 2020
Subject
Language
Abstract
This paper studies the use of independent component analysis (ICA) for reducing the dimensionality of one type of spectro-temporal features, known as the MPEG-7 audio signature descriptors. The dimension-reduced features are used to identify distorted audio items in the experiments. The proposed ICA-based reduction approach is compared with the block average method and the principal component analysis (PCA) method. The experimental results show that features obtained by the ICA approach have higher identification accuracy than comparison counterparts for moderate to highly distorted soundtracks. In this regard, the proposed approach is a better alternative for dimensionality reduction for spectro-temporal features with distortion.