scholarly journals Assessing the effect of noise-reduction to the intelligibility of low-pass filtered speech

2016 ◽  
Author(s):  
Huimin Zhuge ◽  
Lei Wang ◽  
Fei Chen ◽  
Dingchang Zheng
Keyword(s):  
Noise Reduction ◽  
Low Pass ◽  
Filtered Speech

SYNTHESIS OF A DIGITAL LOW-PASS QUASI-GAUSSIAN FILTER FOR NOISE REDUCTION IN POISSON OBSERVATIONS

2021 ◽  
Vol 57 (4) ◽  
pp. 118-125
Author(s):  
V. E. Chinkin ◽  
V. G. Getmanov ◽  
I. I. Yashin
Keyword(s):  
Noise Reduction ◽  
Gaussian Filter ◽  
Low Pass

scholarly journals Effects of Additional Low-Pass–Filtered Speech on Listening Effort for Noise-Band–Vocoded Speech in Quiet and in Noise

2019 ◽  
Vol 40 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Carina Pals ◽  
Anastasios Sarampalis ◽  
Mart van Dijk ◽  
Deniz Başkent
Keyword(s):  
Noise Band ◽  
Listening Effort ◽  
Low Pass ◽  
Filtered Speech ◽  
Vocoded Speech

scholarly journals Modeling Binaural Unmasking of Speech Using a Blind Binaural Processing Stage

2020 ◽  
Vol 24 ◽  
pp. 233121652097563
Author(s):  
Christopher F. Hauth ◽  
Simon C. Berning ◽  
Birger Kollmeier ◽  
Thomas Brand
Keyword(s):  
Speech Recognition ◽  
Speech Intelligibility ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Binaural Processing ◽  
Speech In Noise ◽  
Masking Level Difference ◽  
Low Pass ◽  
Speech Intelligibility Index ◽  
Filtered Speech

The equalization cancellation model is often used to predict the binaural masking level difference. Previously its application to speech in noise has required separate knowledge about the speech and noise signals to maximize the signal-to-noise ratio (SNR). Here, a novel, blind equalization cancellation model is introduced that can use the mixed signals. This approach does not require any assumptions about particular sound source directions. It uses different strategies for positive and negative SNRs, with the switching between the two steered by a blind decision stage utilizing modulation cues. The output of the model is a single-channel signal with enhanced SNR, which we analyzed using the speech intelligibility index to compare speech intelligibility predictions. In a first experiment, the model was tested on experimental data obtained in a scenario with spatially separated target and masker signals. Predicted speech recognition thresholds were in good agreement with measured speech recognition thresholds with a root mean square error less than 1 dB. A second experiment investigated signals at positive SNRs, which was achieved using time compressed and low-pass filtered speech. The results demonstrated that binaural unmasking of speech occurs at positive SNRs and that the modulation-based switching strategy can predict the experimental results.

scholarly journals Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Soojun Kim ◽  
Huiseong Noh ◽  
Narae Kang ◽  
Keonhaeng Lee ◽  
Yonsoo Kim ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Standard Error ◽  
Rainfall Series ◽  
Radar Rainfall ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Filtering Technique ◽  
Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

Efficiency Improvement of Delta Sigma Modulator-Based Transmitter Using Complex Delta Sigma Modulator and Noise Reduction Loop

2020 ◽  
Vol 29 (16) ◽  
pp. 2050267
Author(s):  
Nasser Erfani Majd ◽  
Amin Aeenmehr
Keyword(s):  
Noise Reduction ◽  
Average Power ◽  
Quantization Noise ◽  
Coding Efficiency ◽  
Delta Sigma Modulator ◽  
Delta Sigma ◽  
Low Pass ◽  
Polar Low ◽  
Oversampling Ratio

This paper proposes an architecture to enhance coding efficiency (CE) of the Delta Sigma Modulator (DSM) transmitters. In this architecture, a complex–low pass delta sigma modulator (LPDSM) is used instead of existing Cartesian–LPDSM and polar–low pass envelope delta sigma modulator (LPEDSM). Simulation results show that for Uplink long-term evolution (LTE) signal with 1.92[Formula: see text]MHz bandwidth and 7.8-dB peak to average power ratio (PAPR), the CE for the complex–LPDSM-based transmitter is equal to 41.7% in compare to 9.7% CE for Cartesian–LPDSM transmitter. Also, due to the resolving of noise convolution problem, the complex–LPDSM-based transmitter baseband part needs lower oversampling ratio (OSR) and clock speed than polar–LPEDSM transmitter baseband part to achieve the same signal-to-noise and distortion ratio (SNDR). In the next step, a quantization noise reduction loop is implemented in this architecture. By using this technique for an Uplink LTE signal with 1.92[Formula: see text]MHz bandwidth, with the same PAPR and OSR of 16, the CE is improved from 41.7% to 56.1% with 40[Formula: see text]dB SNDR.

Magnitude-Estimation Scaling: An Effective Method for the Measurement of the Quality of Filtered Speech

1994 ◽  
Vol 78 (1) ◽  
pp. 348-350 ◽  
Author(s):  
Donald Fucci ◽  
Steve Domyan ◽  
Lee Ellis ◽  
Daniel Harris
Keyword(s):  
Magnitude Estimation ◽  
Speech Stimulus ◽  
Stimulus Quality ◽  
Low Pass ◽  
Filtered Speech ◽  
High Pass

17 subjects provided magnitude estimations in the form of quality judgments of a filtered speech stimulus which was a nonsense sentence containing all of the consonants of English from Fairbanks. It was presented to subjects at 8 high-pass and 8 low-pass filtering conditions. Consistent magnitude estimations to the filtered stimulus were similar for both conditions. Also, for both conditions, subjects' numerical responses consistently increased in value as stimulus quality was judged to be poorer.

Coding Efficiency and Bandwidth Enhancement in Polar Delta-Sigma Modulator Transmitter Using Quantization Noise Reduction and Parallel Processing Techniques

2017 ◽  
Vol 26 (05) ◽  
pp. 1750085 ◽  
Author(s):  
Nasser Erfani Majd ◽  
Hassan Ghafoori Fard ◽  
Abbas Mohammadi
Keyword(s):  
Noise Reduction ◽  
Average Power ◽  
Quantization Noise ◽  
Bandwidth Enhancement ◽  
Coding Efficiency ◽  
Clock Speed ◽  
Delta Sigma Modulator ◽  
Delta Sigma ◽  
Low Pass ◽  
Processing Techniques

This paper introduces an architecture to enhance coding efficiency (CE) and bandwidth of the delta-sigma modulator (DSM) transmitters. In this architecture a low-pass envelope DSM (LPEDSM) is used instead of the traditional Cartesian low-pass DSM (LPDSM) to reduce the quantization noise and to improve the coding efficiency. The simulation results show that for an uplink long-term evolution (LTE) signal with 1.92[Formula: see text]MHz bandwidth, 7.8[Formula: see text]dB peak-to-average power ratio (PAPR), and an oversampling ratio (OSR) of 32, the CE for the polar LPEDSM transmitter is equal to 41.72% in comparison to 9.7% CE for the Cartesian LPDSM transmitter. In the next step, the CE and bandwidth of the transmitter are improved at the same time by using the quantization noise reduction technique in the polar LPEDSM transmitter with parallel baseband. By using this combined technique in the four-branch transmitter baseband part for an uplink LTE signal with 7.68[Formula: see text]MHz bandwidth, 7.8[Formula: see text]dB PAPR, and an OSR of 32, the CE is improved from 42.59% to 55.86% with 40[Formula: see text]dB signal-to-noise-and-distortion ratio (SNDR) while the clock speed is only 61.44[Formula: see text]MHz which is four times lower than the clock speed requirement of the conventional transmitter baseband part to achieve the same SNDR.

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