A Pitch Detection Method for Speech Signals with Low Signal-to-Noise Ratio

2007 ◽  
Author(s):  
C. Shahnaz ◽  
W.-P. Zhu ◽  
M. O. Ahmad
Keyword(s):  
Detection Method ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Pitch Detection ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals New Speech Compression Technique based on Filter Bank Design and Psychoacoustic Model

2019 ◽  
Vol 24 (4) ◽  
pp. 728-735
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Compression Ratio ◽  
Filter Bank ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Speech Compression ◽  
Signal To Noise ◽  
Compression Technique ◽  
Psychoacoustic Model ◽  
Perceptual Evaluation ◽  
Noise Ratio

In this paper, a new speech compression technique is proposed. This technique applies a Psychoacoustic Model and a general approach for Filter Bank Design using optimization. It is evaluated and compared with a compression technique using a MDCT (Modified Discrete Cosine Transform) Filter Bank of 32 Filters and a Psychoacoustic Model. This evaluation and comparison is performed by calculating bits before and after compression, PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error), SNR (Signal to Noise Ratio) and PESQ (Perceptual evaluation of speech quality) computations. The two techniques are tested and applied to a number of speech signals that are sampled at 8 kHz. The results obtained from this evaluation show that the proposed technique outperforms the second compression technique (based on a Psychoacoustic Model and MDCT filter Bank) in terms of Bits after compression and compression ratio. In fact, the proposed technique yields higher values for the compression ratio than the second compression technique. Moreover, the proposed compression technique presents reconstructed speech signals with acceptable perceptual qualities. This is justified by the values of SNR, PSNR and NRMSE and PESQ.

Micro-crack detection method of steel beam surface using stacked autoencoders on massive full-scale sensing strains

2019 ◽  
Vol 19 (4) ◽  
pp. 1175-1187 ◽  
Author(s):  
Qingsong Song ◽  
Yu Chen ◽  
Elias Abdoli Oskoui ◽  
Zheng Fang ◽  
Todd Taylor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Full Scale ◽  
Signal To Noise ◽  
High Quality ◽  
Strain Measurements ◽  
Feature Representations ◽  
Noise Ratio

Accurate micro-crack detections on the whole surface of civil structures have great significance. Distributed optical fiber sensor based on Brillouin optical time-domain analysis technology exhibits great facility to measure strain distributions along the whole surface of structures with a high spatial resolution, thus providing a potential and competitive solution to the detection problem. However, mainly due to low signal-to-noise ratio in measurements, such sensor system is still limited in crack detection–based structural health monitoring applications. How to extract high-quality micro-crack feature representations from the low signal-to-noise ratio–distributed strain measurements is crucial to solve the problem. It has been demonstrated in field of pattern recognition that deep learning can automatically extract high-quality noise-robust feature representations from mass chaos data. Therefore, a micro-crack detection method is proposed herein based on deep learning to analyze the full-scale strain measurements. Each measurement is normalized and segmented into a set of equal-length subsequences. Autoencoders, a typical kind of building block of deep neural network, are stacked layer-wise into a deep network and then exploited to automatically extract feature representations from the subsequences. Each extracted feature representation is labeled as one of the two categories by a Softmax regression. One category originates in the subsequences acquired from structure sections with crack defects and another from sections without any cracks. The micro-crack detections are achieved by solving such a crack/non-crack binary classification problem. A 15-m-long steel I-beam with artifact crack defects is built up in laboratory to verify the proposed method. Experimental results demonstrate that the minimum size of detectable crack opening width reaches to 23 μm, and besides, the proposed method is significantly better than traditional Fisher linear discriminant analysis method and classical support vector machine on the detection accuracy.

High Signal-to-Noise Ratio Ultrasonic Point Detection Method using a Fused Quartz Rod as a Pulse Compression Filter and a Sensor

1997 ◽  
Vol 36 (Part 1, No. 5B) ◽  
pp. 3157-3159 ◽  
Author(s):  
Masasumi Yoshizawa ◽  
Norio Tagawa ◽  
Eiki Watanabe ◽  
Tadashi Moriya ◽  
Shin-ichi Yagi
Keyword(s):  
Pulse Compression ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Fused Quartz ◽  
Noise Ratio ◽  
Point Detection

A Detection Method for Overlapped Spikes

2011 ◽  
Vol 267 ◽  
pp. 530-535
Author(s):  
Jia Qi ◽  
Min Dai ◽  
Gang Zheng ◽  
Tong Tong Liu
Keyword(s):  
Detection Method ◽  
Signal To Noise Ratio ◽  
False Negative ◽  
False Negative Rate ◽  
Energy Operator ◽  
Detection Methods ◽  
Signal To Noise ◽  
Threshold Detection ◽  
Noise Ratio ◽  
Nonlinear Energy

A new spike detection method is proposed in order to detect the overlapped spikes. In order to avoid missing overlapped spikes, the method adds threshold detection based on window detection method. Moreover, nonlinear energy operator is introduced to make the method strong even under low signal-to-noise ratio situation. In addition, the method solves the repeated detection problem by estimating slopes. Experiments show that the method is good for any occasion whatever the low signal-to-noise ratio or baseline wander. Especially for the overlapped spikes detection, it has much lower false-negative-rate than other traditional detection methods.

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