Improved line spectral frequency estimation through anti-aliasing filtering

2006 ◽  
Vol 153 (5) ◽  
pp. 610 ◽  
Author(s):  
K. Al-Naimi ◽  
S. Villette ◽  
A. Kondoz ◽  
A.P. Heikkinen
2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Sabur Ajibola Alim ◽  
Nahrul Khair Alang Rashid ◽  
Wahju Sediono ◽  
Nik Nur Wahidah Nik Hashim

Stuttering or stammering is disruptions in the normal flow of speech by dysfluencies, which can be repetitions or prolongations of phoneme or syllable. Stuttering cannot be permanently cured, though it may go into remission or stutterers can learn to shape their speech into fluent speech with an appropriate speech pathology treatment. Linear Prediction Coefficient (LPC), Linear Prediction Cepstral Coefficient (LPCC) and Line Spectral Frequency (LSF) were used for the feature extraction, while Multilayer Perceptron (MLP) was used as the classifier. The samples used were obtained from UCLASS (University College London Archive of Stuttered Speech) release 1. The LPCC-MLP system had the highest overall sensitivity, precision and the lowest overall misclassification rate. LPCC-MLP system had challenges with F3, the sensitivity of the system to F3 was negligible, similarly, the precision was moderate and the misclassification rate was negligible, but above 10%. 


2016 ◽  
Vol 10 (10) ◽  
pp. 1183-1188 ◽  
Author(s):  
Tzu-Hung Lin ◽  
Shun-Chieh Chang ◽  
Cheng-Yu Yeh ◽  
Shaw-Hwa Hwang

2021 ◽  
Vol 19 ◽  
pp. 195-206
Author(s):  
Lorenz J. Dirksmeyer ◽  
Aly Marnach ◽  
Daniel Schmiech ◽  
Andreas R. Diewald

Abstract. With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.


2012 ◽  
Vol 9 (1) ◽  
pp. 885-889
Author(s):  
Cheng-Yu Yeh ◽  
Chun-Cheng Lin ◽  
Long-Jhe Yan

Sign in / Sign up

Export Citation Format

Share Document