基于噪声抑制和层次稀疏表示分类器的现场音乐鲁棒情感识别

Yu-Hao Chin, Chang-Hong Lin, Jia-Ching Wang
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引用次数: 0

摘要

对音乐中情感内容的识别是最近出现的一个问题。实时应用程序接收的音乐经常受到噪声的影响,从而容易降低应用程序的识别率。本研究提出的解决方案是一个鲁棒的音乐情感识别系统,用于现场应用。该系统由基于子空间的噪声抑制和基于稀疏编码和稀疏表示分类器(SRC)的分层稀疏表示分类器两大部分组成。首先采用基于子空间的快速噪声抑制技术增强音乐效果。然后用9类情绪构造字典,通过稀疏编码得到系数向量。这个向量可以分为九个部分,每个部分都模拟了一个信号的特定情感类别。由于所提出的描述符可以为情感音乐识别提供不同分辨率的情感内容分析,因此本文将系数向量作为特征表示。最后,采用基于稀疏表示的分类方法将音乐分为四类情感。实验结果证实了该系统在现场音乐情感识别中的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust emotion recognition in live music using noise suppression and a hierarchical sparse representation classifier
Recognition of emotional content in music is an issue that arises recently. Music received by live applications are often exposed to noise, thus prone to reducing the recognition rate of the application. The solution proposed in this study is a robust music emotion recognition system for live applications. The proposed system consists of two major parts, i.e. subspace-based noise suppression and a hierarchical sparse representation classifier, which is based on sparse coding and a sparse representation classifier (SRC). The music is firstly enhanced by fast subspace based noise suppression. Nine classes of emotion are then used to construct a dictionary, and the vector of coefficients is obtained by sparse coding. The vector can be divided into nine parts, and each of which models a specific emotional class of a signal. Since the proposed descriptor can provide emotional content analysis of different resolutions for emotional music recognition, this work regards vectors of coefficients as feature representations. Finally, a sparse representation based classification method is employed for classification of music into four emotional classes. The experimental results confirm the highly robust performance of the proposed system in emotion recognition in live music.
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