An Impulse Noise Robust Voice Activity Detection Algorithm Applied for Low Signal-to-Noise Ratio Digital Communication

2006 ◽  
Author(s):  
Tong Wang ◽  
Hui-juan Cui ◽  
Kun Tang
Keyword(s):  
Impulse Noise ◽  
Digital Communication ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Noise Robust ◽  
Voice Activity

Speaker Verification Under Degraded Conditions Using Empirical Mode Decomposition Based Voice Activity Detection Algorithm

2014 ◽  
Vol 23 (4) ◽  
pp. 359-378
Author(s):  
M. S. Rudramurthy ◽  
V. Kamakshi Prasad ◽  
R. Kumaraswamy
Keyword(s):  
Speaker Recognition ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Front End ◽  
Different Types ◽  
Voice Activity

AbstractThe performance of most of the state-of-the-art speaker recognition (SR) systems deteriorates under degraded conditions, owing to mismatch between the training and testing sessions. This study focuses on the front end of the speaker verification (SV) system to reduce the mismatch between training and testing. An adaptive voice activity detection (VAD) algorithm using zero-frequency filter assisted peaking resonator (ZFFPR) was integrated into the front end of the SV system. The performance of this proposed SV system was studied under degraded conditions with 50 selected speakers from the NIST 2003 database. The degraded condition was simulated by adding different types of noises to the original speech utterances. The different types of noises were chosen from the NOISEX-92 database to simulate degraded conditions at signal-to-noise ratio levels from 0 to 20 dB. In this study, widely used 39-dimension Mel frequency cepstral coefficient (MFCC; i.e., 13-dimension MFCCs augmented with 13-dimension velocity and 13-dimension acceleration coefficients) features were used, and Gaussian mixture model–universal background model was used for speaker modeling. The proposed system’s performance was studied against the energy-based VAD used as the front end of the SV system. The proposed SV system showed some encouraging results when EMD-based VAD was used at its front end.

Noise-Robust Voice Activity Detector Based on Four States-Based HMM

2013 ◽  
Vol 411-414 ◽  
pp. 743-748 ◽  
Author(s):  
Bin Zhou ◽  
Jing Liu ◽  
Zheng Pei
Keyword(s):  
Fractal Dimension ◽  
Hidden Markov ◽  
Signal To Noise Ratio ◽  
Activity Detection ◽  
Noisy Environments ◽  
Signal To Noise ◽  
Voice Activity Detector ◽  
Noise Robust ◽  
Voice Activity ◽  
Accuracy Performance

Voice activity detection (VAD) is more and more essential in the noisy environments to provide an accuracy performance in the speech recognition. In this paper, we provide a method based on left-right hidden Markov model (HMM) to identify the start and end of the speech. The method builds two models of non-speech and speech instead of existed two states, formally, each model could include several states, we also analysis other features, such as pitch index, pitch magnitude and fractal dimension of speech and non-speech.. We compare the VAD results with the proposed algorithm and two states HMM. Experiments show that the proposed method make a better performance than two states HMMs in VAD, especially in the low signal-to-noise ratio (SNR) environment.

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

2022 ◽  
Author(s):  
William Ferris ◽  
Larry Albert DeWerd ◽  
Wesley S Culberson
Keyword(s):  
Management System ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Position Error ◽  
Signal To Noise ◽  
Motion Management ◽  
Future Studies ◽  
Planar Images ◽  
Noise Ratio

Abstract Objective: Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach: A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results: The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance: This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

Noise robust model-based voice activity detection

2006 ◽  
Author(s):  
Ángel de la Torre ◽  
Javier Ramírez ◽  
Carmen Benítez ◽  
José C. Segura ◽  
L. García ◽  
...  
Keyword(s):  
Voice Activity Detection ◽  
Activity Detection ◽  
Model Based ◽  
Robust Model ◽  
Noise Robust ◽  
Voice Activity
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