Optimized Speech Compression Algorithm Based on Wavelets Techniques and its Real Time Implementation on DSP

Aloui, Noureddine; Bousselmi, Souha; Chérif, Adnane

doi:10.5815/ijitcs.2015.03.05

Cited by 6 publications

(8 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CPU speech and the memory consumption meet the real time processing requirements of TMS320C6416 (CPU speed = 1 GHz, 512KB of flash memory and 16MB of SRAM). Compared to the results obtained in [16], which highlights a real-time implementation of speech compression using wavelet, our work brings some improvements in term of complexity. Due to the accuracy of subjective evaluation, we have considered the listening test ACR.…”

Section: B Rapid Prototyping Technologymentioning

confidence: 97%

“…In recent years many researches in the field of digital signal processing show interests on Discrete Hartley Transform (DHT) [14] [15] [16].Computing the DHT directly from its definition is too slow which does not fit with real-time application in which the computational time has a great importance.In above context, we have proposed a real-time speech compression system based on Fast Hartley Transform (FHT). We have also proposed a modified scheme for Run Length Encoding (RLE) to improve compression factor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DSP Real-Time Implementation of an Audio Compression Algorithm by using the Fast Hartley Transform

Bousselmi¹,

Aloui²,

Cherif³

2017

ijacsa

Self Cite

View full text Add to dashboard Cite

Abstract-This paper presents a simulation and hardware implementation of a new audio compression scheme based on the fast Hartley transform in combination with a new modified run length encoding. The proposed algorithm consists of analyzing signals with fast Hartley Transform and then thresholding the obtained coefficients below a given threshold which are then encoded using a new approach of run length encoding. The thresholded coefficients are, finally, quantized and coded into binary stream. The experimental results show the ability of the fast Hartley transform to compress audio signals. Indeed, it concentrates the signal energy in a few coefficients and demonstrates the ability of the new approach of run length encoding to increase the compression factor. The results of the current work are compared with wavelet based compression by using objective assessments namely CR, SNR, PSNR and NRMSE. This study shows that the fast Hartley transform is more appropriate than wavelets one since it offers a higher compression ratio and a better speech quality. In addition, we have tested the audio compression system on DSP processor TMS320C6416.This test shows that our system fits with the real-time requirements and ensures a low complexity. The perceptual quality is evaluated with the Mean Opinion Score (MOS).

show abstract

Section: B Rapid Prototyping Technologymentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

DSP Real-Time Implementation of an Audio Compression Algorithm by using the Fast Hartley Transform

Bousselmi¹,

Aloui²,

Cherif³

2017

ijacsa

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we present the objective criteria used for evaluation and comparison between the proposed technique and that of Alex et al [10][11][12]. These criteria are bits before and after compression, SNR (Signal to Noise Ratio), PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error) which is detailed in [14,17] and PESQ (Perceptual Evaluation Speech Quality) which are detailed in [18][19].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Those results are in term of bits before and after compression, Compression Ratio (CR) and PESQ. The speech compression system of Aloui et al [14] is based on Discrete [20][21] and integrating a Voice Activity Detection (VAD) Module [14]. According to Aloui et al [14], this VAD module avoids the computation of discrete wavelet coefficients during the inactive voice signal.…”

Section: Without Multiplying By Any Factormentioning

confidence: 99%

“…In our technique evaluation we have also used another research work which a third compression technique based on Discrete Wavelet Transform (DWT) and integrating a Voice Activity Detection System. This latter technique was proposed in [14]. In fact the DWT is a powerful tool used in many domains of signal and image processing such as in [15][16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A psychoacoustic model and a Filter Bank Design using optimization for speech compression

Talbi¹,

Bouhlel²

2018

AMA_B

View full text Add to dashboard Cite

In this paper we propose a new speech compression technique employing psychoacoustic model and a general approach for Filter Bank Design using optimization. This technique is inspired from an audio compression technique using psychoacoustic model and a Modified Discrete Cosine Transform (MDCT) filter banks of 32 filters. In fact, in this proposed approach, we have used Uniform/Non-Uniform Filter Bank (which is designed using optimization) instead of a MDCT filter banks of 32 filters. The two techniques are evaluated and compared with each other by computing bits before and after compression. They are tested and applied to different speech signals. The simulation results obtained from the computation of the compressed files size and the Compression Ratios (CR), show that the proposed technique outperforms the second one. In term of perceptual speech quality, the outputs speech signals of the proposed compression system are with good quality. This is justified by the computation of SNR (Signal to Noise Ratio), PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error) and PESQ (Perceptual evaluation of speech quality). We have also compared the proposed technique to one previous research work which is a speech compression technique based on Discrete Wavelet Transform (DWT) and integrating a Voice Activity Detection (VAD) Module. This comparison is also based on the computation of SNR, PSNR, NRMSE, PESQ and CR and the obtained results show that the proposed technique outperforms this third technique based on DWT and VAD.

show abstract

Real-Time Implementation of an Optimized Speech Compression System in STM32F4 Discovery Board

Bousselmi

Saoud

Cherif

2019

Smart Innovation, Systems and Technologies

Self Cite

View full text Add to dashboard Cite

This paper presents an optimization, and a real-time implementation of a wavelet based speech compression system in STM32F4 discovery card. The optimization is done on the one hand by considering a Voice Activity Detection (VAD) to reduce the complexity and on the other hand by using a new quantization approach that codes each sample with fewer bits. The performance of the embedded audio codec is evaluated with a test technique called Processor-in-the-Loop (PIL) and using objective measures that can predict the perceived quality of the signal, namely SNR, PSNR and MSE. The compression efficiency is measured with the compression factor (CR). This research highlights the importance of the proposed optimizations. Indeed, they increase the CR without damaging the voice quality. The practical study shows that the proposed system meets the temporal and material requirements. Voice clarity is assessed with the Mean Opinion Score (MOS).

show abstract

Optimized Speech Compression Algorithm Based on Wavelets Techniques and its Real Time Implementation on DSP

Cited by 6 publications

References 22 publications

DSP Real-Time Implementation of an Audio Compression Algorithm by using the Fast Hartley Transform

DSP Real-Time Implementation of an Audio Compression Algorithm by using the Fast Hartley Transform

A psychoacoustic model and a Filter Bank Design using optimization for speech compression

Real-Time Implementation of an Optimized Speech Compression System in STM32F4 Discovery Board

Contact Info

Product

Resources

About