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.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Effects of Using Static Methods with Contourlet Transformation on Speech Compression

2021 ◽  
pp. 2784-2795
Author(s):  
Esraa Abd Alsalam ◽  
Shaymaa Ahmed Razoqi ◽  
Eman Fathi Ahmed
Keyword(s):  
Speech Signal ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Speech Compression ◽  
Signal To Noise ◽  
Absolute Deviation ◽  
Limited Bandwidth ◽  
Normalized Cross Correlation ◽  
Noise Ratio ◽  
And Storage

Compression of speech signal is an essential field in signal processing. Speech compression is very important in today’s world, due to the limited bandwidth transmission and storage capacity. This paper explores a Contourlet transformation based methodology for the compression of the speech signal. In this methodology, the speech signal is analysed using Contourlet transformation coefficients with statistic methods as threshold values, such as Interquartile Filter (IQR), Average Absolute Deviation (AAD), Median Absolute Deviation (MAD) and standard deviation (STD), followed by the application of (Run length encoding) They are exploited for recording speech in different times (5, 30, and 120 seconds). A comparative study of performance of different transforms is made in terms of (Signal to Noise Ratio,Peak Signal to Noise Ratio,Normalized Cross-Correlation, Normalized Cross-Correlation) and the compression ratio (CR). The best stable result of implementing our algorithm for compressing speech is at level1 with   AAD or MAD, adopting Matlab 2013a language.

scholarly journals Performance on FBMC-OQAM and OFDM Multicarrier Systems

2019 ◽  
Vol 8 (10) ◽  
pp. 1564-1568
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Filter Bank ◽  
Signal To Noise Ratio ◽  
Cyclic Prefix ◽  
Frequency Division Multiplexing ◽  
Frequency Division ◽  
Signal To Noise ◽  
Multicarrier Systems ◽  
Noise Ratio ◽  
Power Equality

Filter Bank Multi Carrier (FBMC) offers best detestable properties took a gander at over orthogonal frequency division multiplexing (OFDM) to the attack of nonexistent hindrance. FBMC system is a multicarrier structure, particularly sensible for 5G remote correspondences. FBMC beats OFDM as a result of proficient use of the open information move limit and without usage of cyclic prefix (CP). In this paper, we address the issue of remarkable enrollment at the pilot territory and used to audit the channels with pilot picture, in like way consider the fundamental conditions for utilization of the assistant pilot pictures. First and two partner pictures for each pilot plans with power equality uses instead of one picture; it can attainable inspirations driving necessity of OFDM and FBMC depending upon signal to noise ratio (SNR) what's relentlessly possible to improve the introduction of one frivolity pictures by using multiple associate pictures. Finally autonomous the BER execution reenactment results and adornment pilot pictures

Optimization Before Biomedical Image Compression Using CLAHE and DCS

2018 ◽  
Vol 7 (3.27) ◽  
pp. 236
Author(s):  
Satyawati S. Magar ◽  
Bhavani Sridharan
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Image Enhancement ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Biomedical Image ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

In current years, improving the Compression Ratio (CR) in medical imaging is essential and becomes big challenge in the field of biomedical. In that direction we have done optimization before biomedical image compression. For the same we have used the image enhancement techniques. For the enhancement of an image we have used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Decorrelation Stretch (DCS) algorithms. By optimizing an image before compression we have achieved better Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) than existing methods of an image compression. Mainly results are compared with Oscillation Concept method of an image compression with and without optimization.  

Sign in / Sign up

Export Citation Format

Share Document