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Linear Predictive Coding
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- 2. INTRODUCTION • Linear PredictiveCoding (LPC) is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate. It provides extremely accurate estimates of speech parameters, and is relatively efficient for computation. • The most important aspect of LPC is the linear predictive filter which allows the value of the next sample to be determined by a linear combination of previous samples
- 3. • This providesa rate of 64000 bits/second. Linear predictive coding reduces this to 2400 bits/second. At this reduced rate the speech has a distinctive synthetic sound and there is a noticeable loss of quality. However, the speech is still audible and it can still be easily understood. Since there is information loss in linear predictive coding, it is a lossy form of compression. • LPC starts with the assumption that the speech signal is produced by a buzzer at the end of a tube. The glottis (the space between the vocal cords) produces the buzz, which is characterized by its intensity (loudness) and frequency (pitch). The vocal tract (the throat and mouth) forms the tube, which is characterized by its resonances, which are called formants.
- 4. • A signalprocessing is an activity to extract a signal information. Linear Predictive Coding (LPC) is a powerful speech analysis technique and facilitating a features extraction which has a good quality and efficient result for computing. In 1978, LPC uses to make a speech synthesis. LPC doing an analysis with predicting a formant decided a formant from signal called inverse filtering, then estimated an intensity and frequency from residue speech signal. Because speech signal has many variations depending on a time, the estimation will do to cut a signal called frame
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- 7. Preemphasis • On processingof speech signal, preemphasis filter needed after sampling process. The filtering purpose is to get a smooth spectral shape of the speech signal. A spectral which have a high value for the low-frequency field and decrease for field frequency higher than 2000 Hz. Preemphasis filter based on the relation of input/output on time domain which is shown by the equation (1), • • a is a constant of preemphasis filter, ordinary have 0.9 < a < 1.0.
- 8. Frame Blocking Frame Blocking: •On this process, segmented of speech signal become some frame which overlaps. So that no signal is lost (deletion).
- 9. Windowing. • Analog signalwhich converts become digital signal read frame by frame and each frame is windowing with the certain window function. This windowing process purpose to minimize discontinue signal from initial to end of each frame. If window as w(n), 0 ≤ n ≤ N – 1, when N is total of sample of each frame, thus result of windowing is a signal:
- 10. Auto-correlation Analysis • Thenext step is autocorrelation analysis toward each frame result by windowing y1 (n) with equation (4), • (4) Where p is ordered from LPC. LPC order which usually used is between 8 until 16.
- 11. • This stepwill convert each frame from p+1 autocorrelation become compilation of “LPC parameter” • This compilation becomes LPC coefficient or become other LPC transformation. The formal method to change autocorrelation coefficient become parameter LPC compilation called Durbin method, the form as:
- 15. LPC: Vocoder • Ithas two key components: analysis or encoding and synthesis or decoding. The analysis part of LPC involves examining the speech signal and breaking it down into segments or blocks. • Each segment is than examined further to find the answers to several key questions: • Is the segment voiced or unvoiced? • What is the pitch of the segment? • What parameters are needed to build a filter that models the vocal tract for the current segment? LPC analysis is usually conducted by a sender who answers these questions and usually transmits these answers onto a receiver.
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- 18. • Each segmentof speech has a different LPC filter that is eventually produced using the reflection coefficients and the gain that are received from the encoder. • 10 reflection coefficients are used for voiced segment filters and 4 reflection coefficients are used for unvoiced segments. These reflection coefficients are used to generate the vocal tract coefficients or parameters which are used to create the filter. • The final step of decoding a segment of speech is to pass the excitement signal through the filter to produce the synthesized speech signal.