What are Linear Predictive coding, its methods, and analysis? LPC using Autocorrelation, LPC-Vocoder, etc

1. By-
Srishti Kakade
BETA29

2. INTRODUCTION
• Linear Predictive Coding (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 provides a 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 signal processing 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

6. LPC METHODS

7. Preemphasis
• On processing of 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 signal which 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
• The next 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 step will 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
• It has 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.

17. LPC Decoder

18. • Each segment of 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.