Title :
Pitch detection using EMD-based AMDF
Author :
Yuan Zong ; Yumin Zeng ; Mengchao Li ; Rui Zheng
Author_Institution :
Sch. of Phys. & Technol., Nanjing Normal Univ., Nanjing, China
Abstract :
This paper presents a new modified average magnitude difference function (AMDF) based on empirical mode decomposition (EMD) for pitch detection. We call it EMD-based AMDF (EMDAMDF). EMDAMDF inherits lots of advantages successfully from the conventional AMDF and eliminates the falling trend of the AMDF adaptively by means of EMD. Based on EMDAMDF, an effective pitch detection algorithm is proposed. The simulated results on Keele pitch reference database shows that the performance of the proposed EMDAMDF based pitch detection algorithm is obviously better than the original AMDF and its improvements (such as CAMDF and EAMDF) based algorithms.
Keywords :
audio databases; spectral analysis; speech processing; CAMDF based algorithm; EAMDF based algorithm; EMD-Based AMDF; EMDAMDF based pitch detection algorithm; Keele pitch reference database; empirical mode decomposition; modified average magnitude difference function; Accuracy; Databases; Detection algorithms; Empirical mode decomposition; Market research; Signal to noise ratio; Speech; AMDF; EMD; EMDAMDF; pitch detection;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-6248-1
DOI :
10.1109/ICICIP.2013.6568144