IPEEH: Improving pitch estimation by enhancing harmonics. (1st December 2016)

• Record Type: Journal Article
• Authors: Wu, Kebin; Zhang, David; Lu, Guangming
• Is Part Of: Expert systems with applications. Volume 64(2016)
• Page Start: 317
• Page End: 329
• Publication Date: 2016-12-01
• Subjects: Enhancement -- Fundamental frequency detection -- Harmonics -- Improvement -- Pitch; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.08.018
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The reason for pitch detection failures in frequency domain is analyzed: low HNR. We proposed to enhance the harmonics before detecting pitches. A detailed harmonics enhancement approach iPEEH is presented. Both theoretical evidence and implementation details for enhancement are provided. Comprehensive experiments are designed and versatility of iPEEH is verified. 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.