An analysis of content-based classification of audio signals using a fuzzy c-means algorithm
An analysis of content-based classification of audio signals using a fuzzy c-means algorithm
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DOI：
10.1007/s11042-012-1019-y
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发表时间：
2012-02
影响因子：
3.6
通讯作者：
Mohammad A. Haque;Jong-Myon Kim
中科院分区：
文献类型：
--
作者：
Mohammad A. Haque;Jong-Myon Kim
将基于内容的音频信号分类为广泛的类别，如语音、音乐或带噪声的语音，是进一步处理（如语音识别、基于内容的索引或监视系统）之前的第一步。在本文中，我们提出了一种有效的基于内容的音频分类方法，使用模糊c均值（FCM）算法将音频信号分类为广泛的类型。在时域、频域和系数域分析音频信号的不同特征特征，并对每个特征采用高贵的分析评分方法选择最优特征向量。我们利用基于fcm的分类方案，并将其应用于提取的归一化最优特征向量上，以获得高效的分类结果。实验结果表明，该方法的分类性能比现有的音频分类系统提高了11%以上。
Content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FCM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FCM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.