Chirp MFCC作为语音和音频应用中特征的意义
Significance of Chirp MFCC as a Feature in Speech and Audio Applications
AI总结：
本文提出基于chirp z-transform的chirp MFCC特征，通过从chirp幅度谱计算Mel频率倒谱系数以改进频谱表示，实验证明其在语音-音乐分类、说话人识别和语音命令识别任务中均显著优于vanilla MFCC。
摘要：
本文提出了一种基于chirp z-transform（啁啾z变换）的新特征，能够提供对真实底层频谱的改进表示。该特征即chirp MFCC，是通过从chirp幅度谱而非Fourier transform（傅里叶变换）幅度谱中计算Mel频率倒谱系数而得到的。文中讨论了该提议的理论基础，并使用高斯似然乘积进行了实验验证，以证明与vanilla MFCC相比，所提出的chirp MFCC提供了更好的类别分离度。此外，使用三个不同的任务对该特征进行了真实世界评估，即语音-音乐分类、说话人识别和语音命令识别。在所有三个任务中都表明，所提出的chirp MFCC提供了显著的改进。
英文摘要：
A novel feature, based on the chirp z-transform, that offers an improved representation of the underlying true spectrum is proposed. This feature, the chirp MFCC, is derived by computing the Mel frequency cepstral coefficients from the chirp magnitude spectrum, instead of the Fourier transform magnitude spectrum. The theoretical foundations for the proposal, and the experimental validation using product of likelihood Gaussians, to show the improved class separation offered by the proposed chirp MFCC, when compared with vanilla MFCC are discussed. Further, real world evaluation of the feature is performed using three diverse tasks, namely, speech-music classification, speaker identification, and speech commands recognition. It is shown in all three tasks that the proposed chirp MFCC offers considerable improvements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
