Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62070
Title: iPEEH : Improving pitch estimation by enhancing harmonics
Authors: Wu, K
Zhang, D 
Lu, G
Keywords: Enhancement
Fundamental frequency detection
Harmonics
Improvement
Pitch
Issue Date: 2016
Publisher: Pergamon Press
Source: Expert systems with applications, 2016, v. 64, p. 317-329 How to cite?
Journal: Expert systems with applications 
Abstract: Pitch estimation is quite crucial to many applications. Although a number of estimation methods working in different domains have been put forward, there are still demands for improvement, especially for noisy speech. In this paper, we present iPEEH, a general technique to raise performance of pitch estimators by enhancing harmonics. By analysis and experiments, it is found that missing and submerged harmonics are the root causes for failures of many pitch detectors. Hence, we propose to enhance the harmonics in spectrum before implementing the pitch detection. One enhancement algorithm that mainly applies the square operation to regenerate harmonics is presented in detail, including the theoretical analysis and implementation. Four speech databases with 11 types of additive noise and 5 noise levels are utilized in assessment. We compare the performance of algorithms before and after using iPEEH. Experimental results indicate that the proposed iPEEH can effectively reduce the detection errors. In some cases, the error rate reductions are higher than 20%. In addition, the advantage of iPEEH is manifold since it is demonstrated in experiments that the iPEEH is effective for various noise types, noise levels, multiple basic frequency-based estimators, and two audio types. Through this work, we investigated the underlying reasons for pitch detection failures and presented a novel direction for pitch detection. Besides, this approach, a preprocessing step in essence, indicates the significance of preprocessing for any intelligent systems.
URI: http://hdl.handle.net/10397/62070
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2016.08.018
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