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lpc and horn noise detection
- 1. LINEAR PREDICTIVE CODING METHODSAND HORN NOISE DETECTION By P GOPIKRISHNA (12) EASHWAR JEEVAN(15) V V N PRANATHI(30)
- 2. • Introduction tospeech coders. • Speech production and modeling(coding). • Basic Principles of Linear Predictive Analysis. • The Autocorrelation Method. • The Covariance Method. • Solution of the LPC Equations. • Horn noise detection using LPC Contents
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- 4. Waveform Codec • Waveformcodec’s attempt, without using any knowledge of how the signal to be coded was generated, to produce a reconstructed signal whose waveform is as close as possible to the original. • This means that in theory they should be signal independent and work well with non-speech signals. • Generally they are low complexity codec’s which produce high quality speech at rates above about 16 kbits/s. • When the data rate is lowered below this level the reconstructed speech quality that can be obtained degrades rapidly
- 5. Source Codec • Sourcecoders operate using a model of how the source was generated, and attempt to extract, from the signal being coded, the parameters of the model. • It is these model parameters which are transmitted to the decoder. • Source coders for speech are called vocoders, and work as follows. • The vocal tract is represented as a time-varying filter and is excited with either a white noise source, for unvoiced speech segments, or a train of pulses separated by the pitch period for voiced speech. • Therefore the information which must be sent to the decoder is the filter specification, a voiced/unvoiced flag, the necessary variance of the excitation signal, and the pitch period for voiced speech.
- 6. Desirable Properties ofa Speech Coder • Low Bit-Rate • High Speech Quality • Robustness Across Different Speakers / Languages • Robustness in the Presence of Channel Errors • Good Performance on Nonspeech Signals • Low Memory Size and Low Computational Complexity • Low Coding Delay
- 7. Applications of SpeechCoding Digital Transmissions ◦ On wired telephone: Multiplexing Integration of services ◦ On wireless channels: Spectral efficiency For better protection against errors Voice mail/messaging Storage: telephone answering machine Secure phone
- 8. •study human hearing,especially masking •speech production mechanism •speaker characteristics •linguistic code (recognition-synthesis) •thought-to-speech Future in speech coding
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- 12. Speech Spectrum fora Voiced Sound
- 13. current sample time short-term prediction short-term -resonance of vocal tract long-term - periodicity of voiced speech (vocal cord vibration) long-term prediction source filter speech log spectrum
- 14. LPC is shortterm linear prediction that predicts the coefficients based on previous signal samples which can be transmitted instead of speech at a low bit rate than speech itself Linear model of speech productio A(Z)-Analysis Filter 1/A(Z)-Synthesis Filter
- 15. At transmission end-coding Atreception end-decoding i/p-speech o/p-coded speech parameters i/p-coded speech parameters o/p- speech LPC vocoder
- 16. The same principleas in H.Dudley’s Vocoder Used by US Government (LPC-10) - 2.4 kbs LPC vocoder
- 17. Short Term LinearPrediction • The coefficients of H(z)=1/A(z) can be obtained by linear prediction. • Short term analysis on x(n) speech signal – Frames of 10 to 30 ms. • Least square error criterion
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- 20. .)()( )( )()( )()( 2 1 2 m p k nknn m nn n n kmsamsE meE mneme mnsms We seekto minimize the mean squared error signal: LPC Analysis Equations
- 21. p k nkn m nn p k k m nn ka kmsmsamsE 1 1 2 ).,0()0,0( )()()( The minimum mean-squarederror can be expressed as: LPC Analysis Equations
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- 24. . )( )3( )2( )1( )0(...)3()2()1( )3(...)0()1()2( )2(...)1()0()1( )1(...)2()1()0( :asformmatrixinexpressedbecanand 1),(|)(| :)()(i.e. symmetric,isfunctionationautocorreltheSince 2 1 1 pr r r r a a a rprprpr prrrr prrrr prrrr piirakir sokrkr n n n n pnnnn nnnn nnnn nnnn p k nkn nn Autocorrelation Method Levinson Durbinalgorithm is often used to solve these equations by using the properties of toeplitz matrix and linear prediction
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- 28. . )0,( )0,3( )0,2( )0,1( ),()3,()2,()1,( ),3()3,3()2,3()1,3( ),2()3,2()2,2()1,2( ),1()3,1()2,1()1,1( 3 2 1 pa a a a ppppp p p p n n n n p nnnn nnnn nnnn nnnn The resulting covariancematrix is symmetric, but not Toeplitz, and can be solved efficiently by a set of techniques called Cholesky decomposition The Covariance Method
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- 30. Autocorrelation/Covariance Summary 1.Autocorrelation Method =>signal is windowed by a taperin window in order to minimize discontinuities at beginning (predicting speech from zero-valued samples) and end (predicting zero-valued samples from speech samples) of the interval 2. Covariance method => the signal is extended by p samples outside the normal range of to include p samples occurring prior to m=0 (they are available) and eliminates the need for a tapering window; resulting matrix of correlations is symmetric
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- 34. •we have addeda speech signal and known horn noise . • Threshold value observed practically for given horn noise. •LPC residue for horn and silence is low whereas for speech it is high. •The above property is used to develop algorithm to detect horn noise. Horn noise detection
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- 37. REFERENCES: •SPEECH CODING ALGORITHMSFoundation and Evolution of Standardized Coders -WAI C. CHU •Lecture Notes in Speech Production, Speech Coding, and Speech Recognition Mark Hasegawa-Johnson of University of Illinois •Introduction to Speech Processing | Ricardo Gutierrez-Osuna | CSE TAMU(Texas A & M U niversity) Linear Predictive Coding, Jeremy Bradbury, December 5, 2000