Speech enhancement - an enhanced principal component analysis (EPCA) filter approach. (July 2020)

• Record Type: Journal Article
• Authors: Srinivasarao, V.; Ghanekar, Umesh
• Is Part Of: Computers & electrical engineering. Volume 85(2020)
• Publication Date: 2020-07
• Subjects: Kalman filter -- Noise removal -- Principal component analysis -- Signal to noise ratio -- Speech enhancement -- Speech intelligibility; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106657
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The Principal Component Analysis (PCA) is applied over the noisy speech signal before subjecting it to the Kalman filter. The proposed technique is evaluated with the performance parameters like PESQ, STOI and Segmental SNR. The performance of the proposed PCA improved Kalman filter is better than other common existing techniques in speech enhancement. Abstract: Speech enhancement aims at improving the quality of speech in a noisy environment, also in applications such as speech recognition systems, hearing aids, teleconferencing, etc. Researchers have suggested various techniques to improve the quality of speech signal in a noisy environment. Kalman filtering technique showed better performance and was found remarkable in removing noise components. Therefore, this proposed method employs an improved version of the Kalman filter for the removal of noise in speech signals. A Principal Component Analysis (PCA) developed Kalman filter is proposed for enhancing speech intelligibility and quality. The simulation results prove the recommended technique of enhancing speech signals. It is better in performance compared with Modulation Compressive Sensing, Compressed Sensing Frequency, Wiener filter, and Log Minimum Mean Square Error (LogMMSE) in terms of Short-Time Objective Intelligibility (STOI), Segmental Signal to Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ) for different noise levels. Graphical Abstract: Image, graphical abstract