1. By Sarita Jondhale 1 The process of removing the formants is called inverse filtering The remaining signal after the subtraction of the filtered modeled signal is called the residue The number which describe the intensity and frequency of the buzz, the formants, and the residue signal, can be stored or transmitted

2. By Sarita Jondhale 2 Encoding the residue Most successful method is a use of codebook Codebook: is a table of typical residue signals, which is set up by the system designers Analyzer compares the residue to all the entries in the codebook, chooses the entry which is the closest match, and just send the code for that entry

3. By Sarita Jondhale 3 The synthesizer receives this code, retrieve the corresponding residue form the codebook, and uses that to excite the formant filter

4. By Sarita Jondhale 4 Vector Quantization A technique of compressing the data The basic idea of VQ is to reduce the information rate of the speech signal to a low rate through the use of a codebook with a relatively small number of code words

5. By Sarita Jondhale 5 Vector Quantization The output of both filter bank and LPC analysis is in the form of vectors The main idea in VQ is to make vectors look like symbols that we can count The VQ is nothing more than an approximator

6. By Sarita Jondhale 6 Vector Quantization -4 -3 -2 -1 0 1 2 3 4 00 01 10 11 Example of 1-dimensional VQ Every number less than -2 is approximated to -3 and so on.. The approximate values are uniquely represented by 2 bits VQ has rate of 2bits/dimension

7. By Sarita Jondhale 7 Vector Quantization Advantages: –Reduced storage for spectral analysis information –Very efficient –Reduced computation for determining similarity of spectral analysis vectors. –Discrete representation of speech sounds

8. By Sarita Jondhale 8 Vector Quantization Disadvantages: –An inherent spectral distortion in representing the actual analysis vector: as the size of the codebook increases, the size of the quantization error decreases and vice versa –The storage required for the codebook is important: larger the codebook lesser the quantization error but more the storage required for the codebook

9. By Sarita Jondhale 9 Vector Quantization Create a training set of feature vectors Cluster them into a small number of classes Represent each class by a discrete symbol

10. By Sarita Jondhale 10 Vector Quantization We’ll define a –Codebook, which lists for each symbol –A prototype vector, or codeword If we had 256 classes (‘8-bit VQ’), –A codebook with 256 prototype vectors –Given an incoming feature vector, we compare it to each of the 256 prototype vectors –We pick whichever one is closest (by some ‘distance metric’) –And replace the input vector by the index of this prototype vector

11. By Sarita Jondhale 11 Vector Quantization

12. By Sarita Jondhale 12 Vector Quantization A distance metric or distortion metric –Specifies how similar two vectors are

13. By Sarita Jondhale 13 Vector Quantization Simplest: –Euclidean distance –Also called ‘sum-squared error’

14. By Sarita Jondhale 14 Vector Quantization Vector classification procedure: is basically a full search through the codebook to find the “best” match Best match: The quantization error is minimum

15. By Sarita Jondhale 15 How does VQ work in compression? A vector quantizer is composed of two operations. –encoder –decoder.

16. By Sarita Jondhale 16 encoder The encoder takes an input vector and outputs the index of the codeword that offers the lowest distortion. In this case the lowest distortion is found by evaluating the Euclidean distance between the input vector and each codeword in the codebook. Once the closest codeword is found, the index of that codeword is sent through a channel the channel could be a computer storage, communications channel, and so on

17. By Sarita Jondhale 17 decoder When the decoder receives the index of the codeword, it replaces the index with the associated codeword.

18. By Sarita Jondhale 18 Figure 2: The Encoder and decoder in a vector quantizer. Given an input vector, the closest codeword is found and the index of the codeword is sent through the channel. The decoder receives the index of the codeword, and outputs the codeword.

19. By Sarita Jondhale 19 Overview of Auditory Mechanism

20. By Sarita Jondhale 20 Schematic Representation of the Ear

21. By Sarita Jondhale 21 Sound perception The audible frequency range for human is apprx 20Hz to 20KHz The three distinct parts of the ear are outer ear, middle ear and inner ear Outer ear: pinna and Auditory(external) canal Middle ear: tympanic membrane or eardrum Inner ear: cochlea, neural connections

22. By Sarita Jondhale 22 Outer ear: pinna and Auditory(external) canal Middle ear: tympanic membrane or eardrum Inner ear: cochlea, neural connections

23. By Sarita Jondhale 23 Outer ear: –Outer ear includes pinna and auditory canal –It helps to direct an incident sound wave into middle ear –Filters and modifies the captured sound –The perceived sound is sensitive to the pinna’s shape –By changing the pinnas shape the sound quality alters as well as background noise –After passing through ear cannal sound wave strikes the eardrum which is part of middle ear

24. By Sarita Jondhale 24 Middle ear –Ear drum: This oscillates with the frequency as that of the sound wave Movements of this membrane are then transmitted through the system of small bones called as ossicular system From ossicular system to cochlea Cochlea achieves efficient form of impedance matching

25. By Sarita Jondhale 25 Inner ear –It consist of two membranes Reissner’s membrane and basilar membrane –When vibrations enter cochlea they stimulate 20 000 to 30 000 stiff hairs on the basilar membrane –These hair in turn vibrate and generate electrical signal that travel to the brain and become sound –The resonance of the basilar membrane identifies the sound frequency –The intensity of the sound is direct translation of the amplitude of the basilar membrane into excitation of hair cells which in turn fire at higher rates

26. By Sarita Jondhale 26 PinnaAuditory cannal ossicular system cochlea Basilar membrane Tympanic membrane

27. By Sarita Jondhale 27 Basilar Membrane Mechanics

28. By Sarita Jondhale 28 Basilar Membrane Mechanics Characterized by a set of frequency responses at different points along the membrane Different regions of the BM respond maximally to different input frequencies => frequency tuning occurs along BM Mechanical realization of a bank of filters Distributed along the Basilar Membrane is a set of sensors called Inner Hair Cells (IHC) which act as mechanical motion-to-neural activity converters

29. By Sarita Jondhale 29 Mechanical motion along the BM is sensed by local IHC causing firing activity at nerve fibers that innervate bottom of each IHC Each IHC connected to about 10 nerve fibers, each of different diameter => – thin fibers fire at high motion levels, – thick fibers fire at lower motion levels 30,000 nerve fibers link IHC to auditory nerve Electrical pulses run along auditory nerve, ultimately reach higher levels of auditory processing in brain, perceived as sound

30. By Sarita Jondhale 30 measurements of motion along the basilar membrane: Different locations are most sensitive to different frequencies

31. By Sarita Jondhale 31 Auditory model It is the implementation of human Auditory model to machines The auditory model consists of stages for the outer, middle and inner ears. The output of the auditory model is the ensemble interval histogram (EIH) which shares similarities to the auditory nerve response of the mammalian ear. Spectral estimation using auditory models has shown to be efficient and robust but the success of the system depends on the accuracy and robustness of the auditory model used.

32. By Sarita Jondhale 32 EIH model It is a frequency-domain representation, which gives fine low-frequency detail and a grater degree of robustness than conventional spectral representations. The representation is formed from the ensemble histogram of inter-spike intervals in an array of auditory nerve fibers. Useful in isolated word recognition

33. By Sarita Jondhale 33 Ensemble Interval Histogram (EIH)

34. By Sarita Jondhale 34 First stage of linear filters The second stage takes the output of each filter and computes the intervals between the positive crossings of the filtered waveform at various logarithmically spaced thresholds. A histogram of the frequencies corresponding to these intervals is then created. The final stage combines the histograms for each of the channels together into the; final output, the Ensemble Interval Histogram.