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Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 8 Studies and Implementation of Sub Band Coder and Decoder of Speech Signal Sangita Roy, Dola B. Gupta and P.K. Banerjee Abstract--- In the last 40 years a number of coding techniques for analog sources (speech and images) has been employed. Sub band coding is a kind of transform coding in which analog speech signal splitting into a number of different smaller frequency bands. In this paper stress has been given on 64 KBPS telephone line speech signal. By sub banding data rate has been reduced to 12.13804 KBPS. It has been a great achievement from the point of view of data rate reduction which in turn saves bandwidth as well as spectrum. Furthermore this scheme provides acceptable probability of error and quantization noise. In DM bit rate i.e., bandwidth requirement is less than PCM. But from the point of view of signal to noise ratio (SNR), PCM is better than DM, in this dynamic range of SNR at the cost of slightly higher channel bandwidth. In this paper the PCM, DM have been studied which are most popular type of coding techniques used commercially. We have shown that if the SNR output required is above 30 dB, PCM outperforms DM which subsequently used in sub band coding. It is interesting to note that 30 dB is the minimum requirement for communication systems [3]. Keywords--- DM, PCM, SNR, SUBBAND, Probability of Bit Error I. INTRODUCTION S UB-BAND coding (SBC) is a kind of transform coding. A signal is divided into a number of different frequency bands and encodes each one independently. It enables a data reduction / compression by discarding information about frequencies which are masked. The result differs from the original signal, but if the discarded information is chosen carefully, the difference will not be noticeable, or more importantly, objectionable. II. a filter bank (sub-band coding), or by a suitable transform (transform coding), and then encode them using adaptive PCM. Three basic factors of designing of the coders: 1) the type of the filter bank or transform, 2) the choice of bit allocation and noise shaping properties, and 3) the control of the step-size of the encoders. Short-time analysis/synthesis, practical realizations of sub band and transform coding are interpreted within this framework. Spectral estimation, models of speech production, perception and the “side information” can be most efficiently represented and utilized in the design of the coder (particularly the adaptive transform coder) to control the dynamic bit allocation and quantize step-sizes. Recent developments and examples of the „Vocoder-driven” adaptive transform coder for low bit-rate applications is also discussed in [5].In digital Telecommunication system different signals are processed with different sampling rates, these arises significant errors. In ”Sub band Coding of Speech Signals Using Decimation and Interpolation”- a structure of a two-channel quadrature mirror filter with low pass filter, high pass filter, decimators and interpolators, is proposed to perform sub band coding of speech signals in the digital domain. The performance of the proposed structure is compared with the performance of the delta-modulation encoding systems. The results show that the proposed structure significantly reduces the error and achieves considerable performance improvement compared to deltamodulation encoding systems [6]. III. PERFORMANCE OF PCM OVER DM If it is assumed that each of the digital words has n binary digits, there are M= 2n unique code words. If R= bit rate, n = no. of bits in PCM, f s= sampling rate, M= quantizing level, B=bandwidth of analog system, then the bit rate is R=nfs and BPCM > n f s /2 =n B. PCM signal to noise ratio can be expressed as LITERATURE SURVEY The work done in [4] compared SBC and RIQ to conventional coding techniques. The system shows SNR 2 to 5 dB higher than that of other coders of similar computational complexity of wideband audio signals. The basic concept of “Frequency Domain Coding of Speech” methods is to divide the speech into frequency components by Sangita Roy, Electronics and Communication Engineering, Narula Institute of Technology, under West Bengal University of Technology, Agarpara, Kolkata –700 109, India. Dola B. Gupta, Electronics and Communication Engineering, Narula Institute of Technology, under West Bengal University of Technology, Agarpara, Kolkata –700 109, India. P.K. Banerjee, Electronics and Tele communication Engineering Jadavpur University, Garfa Main Road, Jadavpur, Kolkata, West Bengal. (S/N)PCM= 6.02n + α (1) α=0for average, α = 4.77 for peak SNR .This equation is called the 6-dB rule [2]. The S/N of speech signal for the DM system is (2) (S/N)DM =3f 3s ‹ w 2(t) › / ((1600П) 2 B w 2 p) ‹ W 2(t) › /w2p = average audio power to peak audio power ratio. From equations 1 and 2, figures 1 and 2 have been developed where for α = 4.77 PCM overtakes DM after 23.65 dB and for α = 0 after 30 dB. ISBN 978-93-82338-06-2 | © 2012 Bonfring Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 9 compative study of SNR for DM and PM for a=0 70 snr1: DM snr2:PCM 60 snr in dB 50 40 30 20 10 0 0 1 2 3 4 5 6 fs= sampling frequency in HZ 7 8 9 4 x 10 Figure1: Comparative Study of DM and PCM with α=4.77 compative study of SNR for DM and PM for a=0 70 snr1: DM snr2:PCM 60 snr in dB 50 40 30 20 10 0 0 1 2 3 4 5 6 fs= sampling frequency in HZ 7 8 9 4 x 10 Figure 2: Comparative Study of DM and PCM with α=0 Transmitter (figure 5) consists of one LPF and six BPFS. IV. DESIGN PROCEDURE FOR SUB BAND CODING FOR All BPFS outputs are multiplied by the lowest frequency SPEECH SIGNAL (A BASIC SBC SCHEME) component of corresponding bands at the multiplier block. The Power Spectral Density (PSD) model of an Voice The outputs are PCM and then added by summer. Finally the Signal considered is shown in figure 4, and Voice signal has outputs are summed and put into channel. been restricted to 3.5 KHz only. In Figure 4, frequency axis is divided into number of subbands (say 0-f1, f1-f2, f2-f3, f3-f4, etc.). The frequency band (0f1 ) is base band signal whereas ( f1-f2) ,( f2-f3 ),( f3-f4 ) ,etc are band pass signals. Each band will translated to baseband by multiplying with lowest frequency component of the said subband. Here seven subbands have been considered. ISBN 978-93-82338-06-2 | © 2012 Bonfring Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 10 Figure 3: Power Spectral Densities vs. Frequency of Speech Signal Figure 4: Power Spectral Densities vs. Frequency of Speech Signal Using Sub Band Figure 5: Block Diagram of Sub Band Coding Transmitter At the receiver (Figure 6) signals are decoded by seven components and then passed through BPFs of f2 - f1, f3 - f2 etc. decoders. Then each signal is passed through LPF of cut-off Then the outputs are summed up to get the replica of the frequency f1, f2 - f1, f3 - f2 etc . From second to seventh signal original signal. outputs are multiplied by their respective lowest frequency ISBN 978-93-82338-06-2 | © 2012 Bonfring Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 11 Figure 6: Block Diagram of Receiver Figure 7: Staircase Approximation of PSD vs. Frequency of Voice Signal Data rate from the above signal (Figure 7) is reduced from Further data rate can be reduced, if multiplied by the 64 KBPS to 19.5 KBPS, according to the considered model probabilities of occurrences using a practical voice signal of and assumptions. 15 sec. duration (Figure 8). ISBN 978-93-82338-06-2 | © 2012 Bonfring Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 Figure 8: Original Powers Spectral Density Voice Signal of 15 Sec Duration . Figure 9: Probability of Occurrence vs. Frequency of Speech Signal plotting of frequency vs. probabilities of occurence and power spectral densities of voice signal 40 Probability and power spaectral density 35 30 25 20 15 10 y1: Probability of v/s y2: Power Spectral Density of v/s y : Resultant PSD OF V/S 5 0 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 3.5 Figure 10: Resultant PDF and PSD vs. Frequency of Speech Signal ISBN 978-93-82338-06-2 | © 2012 Bonfring 12 Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 13 plotting of frequency vs. Data Rate in KBPS 2.5 r : Data rate Data Rate in KBPS 2 1.5 1 0.5 0 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 3.5 Figure 11: Data Rate vs. Frequency of Figure 10 plotting of frequency vs. Data Rate in KBPS 14 r : Data rate cumur:Cumulative Data Rate 12 Data Rate IN KBPS 10 8 6 4 2 0 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 3.5 Figure 12: Cumulative Data Rate as Well As Data Rate In figure 13 and 14, it have been shown that frequency vs. From Figure 10, data rate can be further reduced to quantization error, SNR and another frequency vs. probability 12.13804 KBPS which is much lower than 19.5 KBPS. of bit error are negligible i.e. within the tolerance limit(10 -5 – Data rate and cumulative data have been shown in the 10-6). figure 11 and 12 respectively. Now it is very essential to find out SNR, quantization noise produced out of sub banding. ISBN 978-93-82338-06-2 | © 2012 Bonfring Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 plotting of frequency vs. Step Size ,Quantization Error Step Size,,quanti.erroe in dB ,signal-to-noise ratio 60 qerrordB snr 50 40 30 20 10 0 -10 -20 -30 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 3.5 Figure 13: Quantization Error, and Signal-To-Noise Ratio plotting of frequency vs. Probability of bit error 1 Probability of bit error 0.8 Probability of bit error 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 Figure 14: Probability of Bit Error vs. Frequency V. PERFORMANCE STUDY Now parameters - bit rate, probability of bit error of sub band speech signal under investigation will be compared with the existing 64 KBPS telephone line. ISBN 978-93-82338-06-2 | © 2012 Bonfring 3.5 14 Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 15 plotting of frequency vs. Data Rate in KBPS 60 Data rate Cumulative Data Rate Existing data rate cumulative existing data rate Data Rate IN KBPS 50 40 30 20 10 0 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 3.5 Figure 15: Comparison of Sub Band Data Rate and Existing Data Rate plotting of frequency vs. Probability Error 1 Probability of Error 64 0.8 0.6 Probability of error 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 0 0.5 1 1.5 2 2.5 voice frequency range in KHz 3 3.5 Figure 16: Probability of Bit Error of Sub Band and 64 KBPS Line vs. Frequency From the above figure it is clear that existing data lines are data rate as well bandwidth savings without losing any of high data rate, requiring more number of bits and large significant information and probability of bit error is also least bandwidth as compared to sub band coding which offers very or may be said negligible. PCM requires high bandwidth as low data rate, less number of bits as well as bandwidth. well as data rate. But PCM and DM have almost same SNR up Another comparative study can be performed from the point of to 30 dB. After 30dB PCM shows performance wise better view of bit error. It is clear that both the proposed scheme and results than DM. It should be stated here again that 30 dB is 64 KBPS have acceptable probability of bit error and the minimum criterion for any communication system. Hence quantization noise. it can be concluded that PCM proves to be better than DM which is again outperformed by Sub band Coding. Further, if more sub bands are used, data rate can be reduced more and VI. CONCLUSION AND FUTURE WORK It is evident from the above discussion that both sub band more accurate approximation of the original voice signal can coding and existing 64 KBPS line have almost negligible be reconstructed. probability of bit error but sub band offers lowest data rate, bandwidth ever possible. Therefore it can be deduced that sub banding generate all the possible significant footsteps towards ISBN 978-93-82338-06-2 | © 2012 Bonfring Proceedings of National Conference on Electronics, Communication and Signal Processing (NCECS 2012), 19th September 2012 VII. ACKNOWLEDGEMENT Authors deeply express their sincere thanks to the Head of the department of ECE for encouraging and allowing them to carry out the project and related support i.e., Simulation laboratory whenever they required. Authors took this opportunity to thank their all faculties who have directly or indirectly helped their paper. Last but not the least they express their thanks to family, friends and colleagues for their cooperation and support. VIII. [1] [2] [3] [4] [5] [6] REFERENCES John G. PROAKIS, Digital Communications, Mc graw-Hill International Edition, fourth edition LEON W. COUCH II, Modern Communication Systems Principles and Applications, Prentice –Hall of India Private Limited,1995 Herbert Taub, Donald L Schilling, Principles of communication systems, Tata Mcgraw-Hill publishing company limited, second edition Yang-Jeng Chen, Robert C. Maher ,Sub-band Coding of Audio using recursively indexed quantization, Department of Electrical Engineering and Center for Communication and Information Science, University of Nebraska-Lincoln Jose M. Tribolet, Member, IEEE, and Ronald E. Crochiere, Senior Member, IEEE, Frequency Domain Coding of Speech, IEEE Transactions on acoustics speech and signal processing, vol.Assp-27, No. 5, October 1979 Ashra f M. Aziz, Sub band Coding of Speech Signals Using Decimation and Interpolation, 13th International Conference on AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009, Military Technical College, Kobry Elkobbah, Cairo, Egypt ISBN 978-93-82338-06-2 | © 2012 Bonfring 16