Robust Speech Hash Function
Robust Speech Hash Function
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DOI：
10.4218/etrij.10.0209.0309
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发表时间：
2010-04
期刊：
影响因子：
1.4
通讯作者：
N. Chen;W. Wan
中科院分区：
文献类型：
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作者：
N. Chen;W. Wan
在这封信中，我们提出了一个新的语音哈希函数的基础上的非负矩阵分解（NMF）的线性预测系数（LPC）。首先，对语音进行线性预测分析以获得其LPC，其代表声道的频率成形属性。然后，对LPC执行NMF以捕获语音的局部特征，然后将其用于哈希向量生成。实验结果表明，所提出的哈希函数在针对各种类型的内容保留信号处理操作的区分力和鲁棒性方面是有效的。
In this letter, we present a new speech hash function based on the non‐negative matrix factorization (NMF) of linear prediction coefficients (LPCs). First, linear prediction analysis is applied to the speech to obtain its LPCs, which represent the frequency shaping attributes of the vocal tract. Then, the NMF is performed on the LPCs to capture the speech's local feature, which is then used for hash vector generation. Experimental results demonstrate the effectiveness of the proposed hash function in terms of discrimination and robustness against various types of content preserving signal processing manipulations.