A Pitch Estimation Algorithm for Speech in Complex Noise Environments Based on the Radon Transform

Li, Bai; Zhang, Xianwu

doi:10.1109/access.2023.3240181

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…In recent years, numerous strategies have been developed to enhance the effectiveness of overcoming and mitigating the impacts of background noise. [27] employs the Radon transform and proposes an innovative approach of pitch estimation for speech in challenging noise environments, integrating the Viterbi algorithm to smooth pitch patterns and mitigating the impact of formants using both logarithmic and power functions. [28] relies on introducing a practical connection between the fundamental frequency (F 0 ) and the instantaneous frequency (F i ).…”

Section: Introductionmentioning

confidence: 99%

Frame Length Dependency for Fundamental Frequency Extraction in Noisy Speech

Rahman,

Chowdury,

Parvin

et al. 2024

JSIP

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The fundamental frequency plays a significant part in understanding and perceiving the pitch of a sound. The pitch is a fundamental attribute employed in numerous speech-related works. For fundamental frequency extraction, several algorithms have been developed which one to use relies on the signal's characteristics and the surrounding noise. Thus, the algorithm's noise resistance becomes more critical than ever for precise fundamental frequency estimation. Nonetheless, numerous state-of-the-art algorithms face struggles in achieving satisfying outcomes when confronted with speech recordings that are noisy with low signal-to-noise ratio (SNR) values. Also, most of the recent techniques utilize different frame lengths for pitch extraction. From this point of view, This research considers different frame lengths on male and female speech signals for fundamental frequency extraction. Also, analyze the frame length dependency on the speech signal analytically to understand which frame length is more suitable and effective for male and female speech signals specifically. For the validation of our idea, we have utilized the conventional autocorrelation function (ACF), and state-of-the-art method BaNa. This study puts out a potent idea that will work better for speech processing applications in noisy speech. From experimental results, the proposed idea represents which frame length is more appropriate for male and female speech signals in noisy environments.

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Section: Introductionmentioning

confidence: 99%

Frame Length Dependency for Fundamental Frequency Extraction in Noisy Speech

Rahman,

Chowdury,

Parvin

et al. 2024

JSIP

View full text Add to dashboard Cite

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“…The key step is obtaining the required geometric information through feature extraction and matching by searching for image pairs with overlapping scenes in 2D image sequences obtained from different views. Methods such as orthogonal polynomials (OPs) [10], [11], Krawtchouk moments [12], discrete tchebichef polynomials [13], [14] and Hahn polynomials [15], [16], [17] can extract image features. According to the obtained geometric information and environmental parameters, the disparity (target depth) is obtained for the 3D reconstruction [18], [19].…”

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confidence: 99%

3D Visualization of Pulmonary Vessel Based on Low-Cost Segmentation and Fast Reconstruction

et al. 2023

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Real-time visual-aided navigation and path strategy for pneumonoconiosis and efficient 3D visualization of pulmonary vessels are of great research and clinical significance in the treatment of lung diseases. The complex structure of lung tissue limits the application of deep learning in pulmonary vascular visualization due to the lack of vascular labeling datasets. Also, the existing methods have large computational complexity and are low efficiency. This study proposes a method for high-quality 3D visualization of pulmonary vessels based on low-cost segmentation and fast reconstruction, consisting of three steps: 1) Pulmonary vessel feature extraction from lung CT images using self-supervised learning, 2) Segmentation of pulmonary sparse vessels in lung CT images using self-supervised transfer learning, and 3) 3D reconstruction of pulmonary vessels based on segmentation results of step (2) using interpolation. The accuracy of pulmonary vascular contour segmentation was improved from 91.31% using the sparse coding to 98.65% using our proposed method (27,270 test sample points); the classifier evaluation accuracy was improved from 95.33% to 98.26%, and the average running time of the model with the test set data was 44 ms per slice. the segmentation results can automatically generate a complete vascular tree model with an average time of 10.8s ± 1 1.6s. The results demonstrate that the proposed method provides fast and accurate 3D visualization of pulmonary vessels, and is promising for more precise and reliable information for pneumonoconiosis patients.

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