# International Journal of Advanced Engineering Technology E-ISSN

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3 3. CELP SPEECH CODER The CELP block diagram is shown in Fig. 3. Voice signal input is the conversion result of human voice an analog signal to digital signal which is carried out by ADC (Analog to Digital Converter). The buffer process and LP (Linear Prediction) analysis is used to estimate the vocal impulse response system at each frame, which then produces pitch delay and LP coefficient. The pitch delay will be used in the pitch synthesis filter and the LP coefficient (ai) at LP Figure 2 Block diagram of AMR codec operation synthesis filter. Before the process of pitch synthesis filter, pitch filter coefficient (b) will be produced first from the computation using the pitch delay (P).The process of LP synthesis filter before the processing of LP coefficient in the block is carried out by converting LP coefficient (ai) to become the reflection coefficient of LP [5]. To obtain gain parameter (θ 0 ) and codebook index (k), perceptual weighting filter process will be done and then error minimization process is carried out. Figure 3 CELP speech coder

4 In the error minimization block, gain and codebook index which will be used in the next block will be determined [5]. After all the parameters are obtained, voice compression process can be carried out. Each block will work as shown in Fig. 3. Therefore, the compressed voice signal can be formed and adapted to the AMR codec mode. 4. OBJECTIVE EVALUATION OF PROPOSED CODER In this paper waveform based and perceptual based analysis of proposed coder has been carried out. 4.1 Waveform based analysis The following parameters are evaluated in this category. (1) Absolute Error (ABS) is mathematically defined as (2) Mean Square Error (MSE) is mathematically expressed as (1) (2) (3) Signal to Noise Ratio is mathematically defined as (3) Where Si= input signal, So= decoded signal and N= total no. of frames 4.2 Perceptual based analysis The following is the important parameter for performing perceptual based analysis. (1) Perceptual Evaluation of Speech Quality (PESQ) In comparison with other objective measures, the PESQ measure is the most complex to compute and is the one recommended by ITU-T P.862 for speech quality assessment of 3.2 khz (narrow-band) handset telephony and narrow-band speech Codecs [10]. PESQ score is computed as a linear combination of the average disturbance value asymmetrical disturbance values and the average as follows: (4) Where and [10]. 5. SIMULATION OF CELP BASED AMR CODER USING MATLAB To compare the performance of each AMR codec mode of operation (defining different bit rates), simulation using MATLAB program is carried out with bit allocation as shown in Table I. The simulation is carried out using CELP speech coding technique and is in sync to the bit rate used in AMR as per ETSI. Partial programming and tweeting in MATLAB helped to construct an e-test bench, that will help simulate and produce results through tables and graphs. The simulations considered Standard GSM Cellular System architectural configuration, having a 20ms mother frame, consisting of four subframes within the mother frame, with each sub-frame having a length of 5ms. As can be seen in Fig. 4, Speech S i (i) and C/I ratio are provided as inputs to the MATLAB e-test bench of CELP based AMR Codec. Depending upon the provided C/I ratio, the mode of AMR Codec is selected. MATLAB program has provision to continuously monitor the change in C/I ratio so that an appropriate AMR full rate mode is selected for subsequent frames [9]. After selection of Codec mode LP analysis is done and the parameters like Frame length (N), Block length (L), Order of filter (M), LP parameter (c), Codebook index (Cb) and Pitch index (Pidx) are computed and provided to CELP analysis part of AMR Codec. Information parameters, as a result of CELP analysis, relating to the AMR codec include: linear prediction coefficient (a), pitch lag (p), codebook index (k), gain (θ 0 ) and pitch filter coefficient (b), been investigated, studied and recorded through the stimulation e-test bench created using MATLAB. The bit allocations of these five information parameters, using scalar and vector quantization, are shown (horizontally) in Table I. As each AMR codec mode has different bit allocation, the total bits is in accordance with the bit allocation for AMR codec as per ETSI standards Recovered speech S o (i) can then be reproduced from these coded data bits by passing them through CELP synthesis filter as shown in Fig. 4.

5 Table 1 AMR bitrate selection according to parameters of CELP Coder AMR mode a p K Θ 0 B Total Bits (Kbps) ,8,8,8 17,17,17,17 15,15,15,15 15,15,15, ,8,8,8 15,15,15,15 11,11,11,11 11,11,11, ,8,8,8 12,12,12,12 7,7,7,7 7,7,7, ,8,8,8 11,11,11,11 7,7,7,7 5,5,5, ,8,8,8 10,10,10,10 5,5,5,5 5,5,5, ,8,8,8 9,9,9,9 4,4,4,4 3,3,3, ,8,8,8 8,8,8,8 3,3,3,3 2,2,2, ,8,8,8 7,7,7,7 2,2,2,2 2,2,2,2 95 Figure 4 MATLAB implementation of CELP based AMR codec 6. OBJECTIVE EVALUATION OF PROPOSED CODER The objective performance evaluation of speech files includes calculation of parameters like Absolute Error, Mean Square Error, Signal to Noise Ratio and Perceptual Evaluation of Speech Quality respectively. Three wave files are used here for the purpose of this analysis, they are: Voice.wav, Five.wav and Doormono.wav. The Voice.wav having samples, while Five.wav and Doormono.wav having samples 4329 and respectively. Equations utilized to calculate the above parameters are as inked in section 4. MATLAB simulated graphical resulting plots are shown in Fig. 5, 6, 7 & 8. Results obtained by the objective analysis are found to be satisfactory as can be judged from figures cited at below.

6 Figure 5 Calculation of ABS ERR for different wave files at various bit-rates of AMR codec Figure 6 Calculation of MSE for different wave files at various bit-rates of AMR codec

7 Figure 7 Calculation of SNR for different wave files at varioous bit-rates of AMR codec Five.wav Doormono.wav Voice.wav Figure 8 PESQ Score for different wave files at various bit-rates of AMR codec 7. DISCUSSIONS AND CONCLUSIONS The results of the simulator study reveal that integrating scattered blocks (single-monolithic whole) makes it interactive (via feedback) that eventually helps to provide optimal solution. The present AMR techniques and advancements reveal the recent trend to develop telecommunication signal processing algorithms as a one monolithic whole [7], effectively, eliminating old standards that consist of several scattered independent processing blocks. Our simulation study is a step in this direction, which clear definition of integration of various functional blocks, as depicted in Figure 4. In stark contrast, the different blocks are now developed together as one

8 monolithic whole, and interact (via feedback) with each other. This enables the designers to provide optimal solutions. Besides, AMR is a huge system that supplies a multiplicity of Codec s to enable the GSM standard to adapt to the numerous conditions and applications in wireless communications. AMR increases the robustness under channel errors (due to changing channel conditions) and limits the degradation of speech quality under background noise as compared to the other GSM coders like Full Rate, Half Rate and Enhanced Full Rate Coders. As can be seen from the obtained results and graphs, it is possible to produce variable bit-rates in CELP coder by changing coefficients of the coder. Despite the fact that insufficient total bit allocation has occurred due to the prediction signal, under the circumstances, the resulting quality of each codec mode is still good and can be heard clearly. The higher the bit-rate used, the better the speech quality. As seen in Fig.5, 6, 7 & 8 CELP based AMR Codec provides acceptable values for various parameters in the objective analysis part when implemented in MATLAB with different wave files. SNR and PESQ improve with increase in bit-rate from 4.75Kbps to 12.2Kbps. Also, reduction in Absolute Error and Mean Square Error is clearly visible with increase in bit-rates. 8. REFERENCES 1. D. Malkovic, Speech Coding Methods in Mobile Radio Communication Systems, 17 th International Conference on Applied Electromagnetics and Communications, oct- 2003, Croatia 2. M. Budagavi, Speech Coding in Mobile Radio Communication, Proceedings of IEEE, Vol. 86, No. 7, July Jerry D. Gibson, Speech Coding Methods, Standards and Applications, IEEE Circuits and Systems Magazine, IEEE Fourth Quarter L. Besacier, GSM Speech Coding and Speaker Recognition, University of Neuchatel, A.L.Breguet, Switzerland 5. Xiao Jianming, Li Xun, Wan Lei, Software Simulation in GSM Environment of Federal Standard 1016 CELP Vocoder, International Conference on Communication Technology, Oct ,1998, Beijing, China 6. Eko Pryadi, Kuniwati Gandi, Herman Kanalebe. Speech Compression Using CELP Speech Coding Technique In GSM AMR, IEEE Conference, Jie Yang, Sheng sheng Yu, Mian Zhao The Implementation and Optimization Of AMR Speech Codec On Dsp, 2007 International Symposium on Intelligent Signal Processing and Communication Systems 8. T.Lundberg Et. Al. Adaptive Thresholds for AMR Codec Mode Selections, IEEE Conference K.Jarvinen Standardization Of the Adaptive Multi Rate Codec, IEEE Conference Yi Hu, Philipos C. Loizou Evaluation of Objective Quality Measures for Speech Enhancement, IEEE Transactions on Audio, Speech and Language processing, Vol. 16, No. 1, Jan

DOI 10.1007/s10772-012-9178-9 Implementation of variable bitrate data hiding techniques on standard and proposed GSM 06.10 full rate coder and its overall comparative evaluation of performance Ninad Bhatt

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