1 / 24

Speech Coding Using LPC

Speech Coding Using LPC. What is Speech Coding. Speech coding is the procedure of transforming speech signal into more compact form for Transmission Available Bandwidth Encryption. Uncompressed Speech signal. Analog speech is a bandpassed signal between 200 and 3400 Hz.

afrodite
Download Presentation

Speech Coding Using LPC

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Speech CodingUsing LPC

  2. What is Speech Coding • Speech coding is the procedure of transforming speech signal into more compact form for • Transmission • Available Bandwidth • Encryption

  3. Uncompressed Speech signal • Analog speech is a bandpassed signal between 200 and 3400 Hz. • Uncompressed digital speech is a bit stream at 64kB/s. • Transmission technology must • transmit the signals from point A to point B: • with minimum degradation • using minimum bandwidth

  4. Speech coding • By coding we mean an efficient representation of the signal – COMPRESSION • The main approaches: • waveform coding • transform coding • Parametric / hybrid coding } smart quantizers

  5. How each of these works: • Waveform coders:try to find an efficient representation of the waveform, directly. • Transform coders: try to find an efficient representation in the frequency domain. • Parametric coders: try to find a small set of parameters that are an efficient representation of the signal. { FFT, etc. speech exc.

  6. Comparison of speech coders

  7. LPC (Linear Predictive coding) • LPC is a model for signal production: it is based on the assumption that the speech signal is produced by a very specific model.

  8. Speech Production in Humans • The speech signal is created by: • A pressure source (lungs), exciting ... • A Filter (Vocal tract: pharynx - mouth [soft palate, tongue] - nasal cavity)

  9. For DSP Engineer • An excitation source • A time varying filter filter: speech Excitation H(t, )

  10. The model and its representation • The LPC model looks at speech as: • Excitation: • periodic (voiced) - originating in the larynx • noise (unvoiced) - fricative, produced in the mouth • An all-pole filter representing the vocal tract all pole filter: . . . . H( )

  11. Block Diagram

  12. Why the name “Linear Predictive Coding” • It is assumed that the new sample is the weighted linear combination of previous samples

  13. Z-Plane Representation • In the z-plane we can write the model as a transfer function: • Clearly this transfer function has only poles - which is why it represents an all pole filter.

  14. Mathematical analysis • Reminder: our problem is to find the LPC parameters, for a given speech signal. This is called the Inverse Problem. • How do we find the set of parameters that gives the best match to the signal?

  15. What are these Parameters • The Coefficients of the All Pole Filter • Pitch of the speech

  16. How do we find the Coefficients: • least squares • Formulation: • Given a signal s(n); • Defining an error as: • Find the set of that will minize the mean square error:

  17. Solution: • Simply equate the derivative of E to zero: • Which gives us the Normal Equations: • These are no more than p linear equations in p unknowns...

  18. Or in matricial form:

  19. What is each element of the form- • A correlation;in other words: • take the signal, multiply it by a shifted version, and sum. • Since our signal is long and time varying- we did it on short windows • Two variants: • autocorrelation method • covariance method

  20. Solving the Matrix • Found the Coefficients a(i) by Using the Levinson-Durbin recursion method

  21. Second Parameter • Pitch was found by the finding the correlation of the signal window with itself • Then these parameters were transmitted

  22. Bit rate for plain LPC vocoder

  23. Bit rate for voice-excited LPC vocoder with DCT

  24. Conclusion • Sound produced through LPC method is not exactly the real sound but it sounds intelligibly understandable • LPC can be used in Speech recognition systems • LPC was widely used in Military because of low bit rate in transmission • There are many variants over the basic scheme: LPC-10, CELP, MELP, RELP, VSELP, ASELP, LD-CELP...

More Related