Speech compression using loosy predictive coding (lpc)
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The paper presents a system for encoding speech at a low bit rate using Loosy Predictive Coding (LPC). LPC uses a 10th order Levinson-Durbin recursion algorithm to accurately estimate speech parameters in a computationally efficient manner. Speech from both male and female speakers was coded. The system was able to code speech at relatively low bit rates while maintaining good quality. LPC models human speech production and can achieve a bit rate of 2400 bits/second, making it suitable for secure telephone systems where meaning is prioritized over quality. LPC breaks sound into segments, sending information on voicing, pitch, and vocal tract to the decoder to reproduce the original speech.
![Base paper: - http://www.mirlabs.org/nagpur/paper16.pdf
Conclusion:
Linear Predictive Coding is an analysis/synthesis technique to lossy speech compression that attempts to
model the human production of sound instead of transmitting an estimate of the sound wave. Linear
predictive coding achieves a bit rate of 2400 bits/second which makes it ideal for use in secure
telephone systems. Secure telephone systems are more concerned that the content and meaning of
speech, rather than the quality of speech, be preserved. The trade-off for LPC’s low bit rate is that it
does have some difficulty with certain sounds and it produces speech that sound synthetic.
Linear predictive coding encoders break up a sound signal into different segments and then send
information on each segment to the decoder. The encoder send information on whether the segment is
voiced or unvoiced and the pitch period for voiced segment which is used to create an excitement signal
in the decoder. The encoder also sends information about the vocal tract which is used to build a filter
on the decoder side which when given the excitement signal as input can reproduce the original speech.
Reference:
[1] V. Hardman and O. Hodson. Internet/Mbone Audio (2000) 5-7.
[2] Scott C. Douglas. Introduction to Adaptive Filters, Digital Signal Processing Handbook (1999) 7-12.
[3] Poor, H. V., Looney, C. G., Marks II, R. J., Verdú, S., Thomas, J. A., Cover, T. M. Information Theory.
The Electrical Engineering Handbook (2000) 56-57.
[4] R. Sproat, and J. Olive. Text-to-Speech Synthesis, Digital Signal Processing Handbook (1999) 9-11 .
[5] Richard C. Dorf, et. al.. Broadcasting (2000) 44-47.
[6] Richard V. Cox. Speech Coding (1999) 5-8.
[7] Randy Goldberg and Lance Riek. A Practical Handbook of Speech Coders (1999) Chapter 2:1-28,
Chapter 4: 1-14, Chapter 9: 1-9, Chapter 10:1-18.
[8] Mark Nelson and Jean-Loup Gailly. Speech Compression, The Data Compression Book (1995) 289-319.
[9] Khalid Sayood. Introduction to Data Compression (2000) 497-509.
[10] Richard Wolfson, Jay Pasachoff. Physics for Scientists and Engineers (1995) 376-377.](https://image.slidesharecdn.com/speechcompressionusingloosypredictivecodinglpc-130830114822-phpapp01/85/Speech-compression-using-loosy-predictive-coding-lpc-5-320.jpg)
Speech compression using loosy predictive coding (lpc)
- 1. Base paper: - http://www.mirlabs.org/nagpur/paper16.pdf Speech Compression using Loosy Predictive Coding International Journal of Emerging Technology and Advanced Engineering Abstract: The aim of the project is to develop a system for encoding good quality speech at low bit rate. To implement this we have used most powerful speech analysis technique called Loosy Predictive Coding (LPC). It uses 10 th order Levinson – Durbin Recursion algorithm to accomplish the task. It provides extremely accurate estimates of speech parameters, and is relatively efficient for computation. The speech signal of males and females were coded. The tradeoffs between the bit rate, end-to-end delay, speech quality, and complexity were analyzed. The results show that project was successful in coding the speech signal at relatively low bit rates with good quality.
- 2. Base paper: - http://www.mirlabs.org/nagpur/paper16.pdf (a) Block Diagram of an LPC Vocoder (b) Mathematical Model of Speech Production
- 3. Base paper: - http://www.mirlabs.org/nagpur/paper16.pdf (c) Human vs. Voice Coder Speech Production
- 4. Base paper: - http://www.mirlabs.org/nagpur/paper16.pdf A: - Waveforms B: - Spectrograms
- 5. Base paper: - http://www.mirlabs.org/nagpur/paper16.pdf Conclusion: Linear Predictive Coding is an analysis/synthesis technique to lossy speech compression that attempts to model the human production of sound instead of transmitting an estimate of the sound wave. Linear predictive coding achieves a bit rate of 2400 bits/second which makes it ideal for use in secure telephone systems. Secure telephone systems are more concerned that the content and meaning of speech, rather than the quality of speech, be preserved. The trade-off for LPC’s low bit rate is that it does have some difficulty with certain sounds and it produces speech that sound synthetic. Linear predictive coding encoders break up a sound signal into different segments and then send information on each segment to the decoder. The encoder send information on whether the segment is voiced or unvoiced and the pitch period for voiced segment which is used to create an excitement signal in the decoder. The encoder also sends information about the vocal tract which is used to build a filter on the decoder side which when given the excitement signal as input can reproduce the original speech. Reference: [1] V. Hardman and O. Hodson. Internet/Mbone Audio (2000) 5-7. [2] Scott C. Douglas. Introduction to Adaptive Filters, Digital Signal Processing Handbook (1999) 7-12. [3] Poor, H. V., Looney, C. G., Marks II, R. J., Verdú, S., Thomas, J. A., Cover, T. M. Information Theory. The Electrical Engineering Handbook (2000) 56-57. [4] R. Sproat, and J. Olive. Text-to-Speech Synthesis, Digital Signal Processing Handbook (1999) 9-11 . [5] Richard C. Dorf, et. al.. Broadcasting (2000) 44-47. [6] Richard V. Cox. Speech Coding (1999) 5-8. [7] Randy Goldberg and Lance Riek. A Practical Handbook of Speech Coders (1999) Chapter 2:1-28, Chapter 4: 1-14, Chapter 9: 1-9, Chapter 10:1-18. [8] Mark Nelson and Jean-Loup Gailly. Speech Compression, The Data Compression Book (1995) 289-319. [9] Khalid Sayood. Introduction to Data Compression (2000) 497-509. [10] Richard Wolfson, Jay Pasachoff. Physics for Scientists and Engineers (1995) 376-377.