SPEECH COMPRESSION USING LPC

Prepared By,Disha Modi,

Roll No: 15MECC12, M.Tech (Communication),Electronics and Communication Department, NU-IT

Table of Content

• Objective

• Introduction to LPC

• LPC system implementation

• Applications

• Simulation results

• Conclusion

Objective:• The past decade has observed progress towards the

submission of low-rate speech coders to public and military communications.

• In cellular telephony standards, service providers are unceasingly met with the challenge of accommodating more users within a limited allocated bandwidth in mobile communication services.

• For this object, service providers are constantly in search of low bit-rate speech coders that deliver high-quality speech.

Introduction to LPC• Human speech is produced in the vocal tract. The linear

predictive coding (LPC) model is based on the vocal tract characterized by this tube of a varying diameter and it represented in mathematical approximation.

• The important facet of LPC is the linear predictive filter which determines the value of the next sample by a linear combination of previous samples.

• One important thing about speech production is that mechanically there is a high correlation between adjacent samples of speech.

• It is a lossy form of compression.

LPC System Implementation• LPC has two key components: 1. Analysis / encoding 2. Synthesis / decoding • LPC Analyzing/encoding• The encoding part of LPC includes observing the speech

signal and break down it into segments.

• LPC encoder block diagram

LPC System Implementation ...(cont’d)Input speech: Under the normal situation, the input signal

is sampled at a rate of 8000 samples per second. This input signal is then break down into approx. 180 sample segments and it is transmitted to the receiver. This means that each segment represents 22.5 milliseconds of the input speech signal.

Voice/Unvoiced Determination: It is important to determine if a segment is voiced or unvoiced because voiced sounds have a distinct waveform then unvoiced sounds. The LPC encoder informs the decoder if a signal segment is voiced or unvoiced by sending a single bit.

LPC System Implementation ...(cont’d)Pitch Period Estimation: The pitch period can be thought

of as the period of the vocal cord vibration that happens during the construction of voiced speech. One type of algorithm takes advantage of the fact that the autocorrelation of a period function, Rxx(k), will have a maximum when k is equivalent to the pitch period.

Vocal Tract Filter: The filter that is used by the decoder to re-form the original input signal is formed based on a set of coefficients. In order to find the filter coefficients that best match the current segment being examined the encoder tries to minimize the mean squared error.

=

LPC System Implementation ...(cont’d)

• E[]=0• -2E[]=0

• In autocorrelation, each E is converted into an autocorrelation function of the form Ryy(k) can be expressed as follows.

• Using Ryy(k), the M equations that were acquired can be written in matrix form RA = P where A is filter coefficients

LPC System Implementation ...(cont’d)• In order to determine the filter coefficients, the equation A

= P must be solved.• The Levinson-Durbin (L-D) Algorithm is a recursive

algorithm that is considered very computationally efficient since it takes advantage of the properties of R when determining the filter coefficients.

• LPC Synthesis/decoding

LPC synthesizer/decoder block diagram

LPC System Implementation ...(cont’d)

• The process of decoding a sequence of speech segments is the reverse of the encoding process. Each segment is decoded individually.

• 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.

• The final step of decoding a segment of speech is to pass the excitement signal through the filter to produce the synthesized speech signal.

Applications• Standard telephone systems• Voice mail systems• Telephone auto answering machines• Text to speech synthesis• Multimedia•  Used in the tonal analysis of violins and other stringed

musical instruments• SILK audio codec • other lossless audio codecs

SIMULATION RESULTS

Female Original Voice

SIMULATION RESULTS ...(cont’d)

SIMULATION RESULTS ...(cont’d)

Male Original Voice

SIMULATION RESULTS ...(cont’d)

SIMULATION RESULTS ...(cont’d)• Performance measurements of LPC compressed signals

PARAMETER MALE FEMALE

Sampling Rate 8000 8000

File length (in seconds) 2.07 2.77

Length of Original Signal 99328 133120

Length of Constructed Signal 97920 132480

SNR(in dB) 17.077 14.77

Compression Ratio 0.9858 0.9952

SIMULATION RESULTS ...(cont’d)• Looking at the SNR computed in Table, it is obvious that

both male and female sounds are noisy as they have a low SNR value.

• It observed that for all levels of compression the quality is better with male signal than female signal.

• On the other hand the compression factor with female signal has larger values comparable with these of male signal. This result is expected because the female voice has more high frequencies than male voice.

• It has observed that no further enhancements can be achieved beyond certain level of decomposition for both signals.

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 from 8000vbits/second in cellular communication 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.

Thank You!

L-D Algorithm• The basic simple ideas behind the recursion are first that it

is easy to solve the system for k =1, and second that it is also very simple to solve for a k +1 coefficients sized problem.

• We are looking for = so that = with = and is not necessary at this stage. The dot product of the second line of gives

• + = 0• Therefore,• and +• Solving the size K+1 Problem• Suppose that we have solved the size k problem and have

found , and .

L-D Algorithm …(Cont’d)• Then we have• has one more row and column than so we cannot apply it

directly to , however if we expend with a zero and call this vector we can apply to it and we get the following interesting result

• Since the matrix is symmetric, we also have something remarkable when reversing the order of coefficients of and calling this vector.

• We can notice that a linear combination is of the form wanted for since the first element is a 1 for all values of . Now if there was a value of for

• Calculating ) gives