Submit by:

Banzadio salazaku (ASU2013010100016)

Submit to: Mrs Kavita Jindal

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1 Overview

Voice recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

This document describes how to build a simple, yet complete and representative

automatic speaker recognition system. Such a speaker recognition system has potential in many security applications. For example, users have to speak a PIN (Personal Identification Number) in order to gain access to the laboratory door, or users have to speak their credit card number over the telephone line to verify their identity. By checking the voice characteristics of the input utterance, using an automatic speaker recognition system similar to the one that we will describe, the system is able to add an extra level of security.

1 Principles of Speaker Recognition

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Speaker recognition can be classified into identification and verification. Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker verification, on the other hand, is the process of accepting or rejecting the identity claim of a speaker. Figure 1 shows the basic structures of speaker identification and verification systems. The system that we will describe is classified as text-independent speaker identification system since its task is to identify the person who speaks regardless of what is saying.

At the highest level, all speaker recognition systems contain two main modules (refer to Figure 1): feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers. We will discuss each module in detail in later sections.

(a) Speaker identification

(b) Speaker verification

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Inputspeech

Featureextraction

Referencemodel

(Speaker #1)

Similarity

Referencemodel

(Speaker #N)

Similarity

Maximumselection

Identificationresult

(Speaker ID)

Referencemodel

(Speaker #M)

SimilarityInputspeech

Featureextraction

Verificationresult

(Accept/Reject)Decision

ThresholdSpeaker ID(#M)

Figure 1. Basic structures of speaker recognition systems

All speaker recognition systems have to serve two distinguished phases. The first one is referred to the enrolment or training phase, while the second one is referred to as the operational or testing phase. In the training phase, each registered speaker has to provide samples of their speech so that the system can build or train a reference model for that speaker. In case of speaker verification systems, in addition, a speaker-specific threshold is also computed from the training samples. In the testing phase, the input speech is matched with stored reference model(s) and a recognition decision is made.

Speaker recognition is a difficult task. Automatic speaker recognition works based on the premise that a person’s speech exhibits characteristics that are unique to the speaker. However this task has been challenged by the highly variant of input speech signals. The principle source of variance is the speaker himself/herself. Speech signals in training and testing sessions can be greatly different due to many facts such as people voice change with time, health conditions (e.g. the speaker has a cold), speaking rates, and so on. There are also other factors, beyond speaker variability, that present a challenge to speaker recognition technology. Examples of these are acoustical noise and variations in recording environments (e.g. speaker uses different telephone handsets).

2 Speech Feature Extraction

2.1 Introduction

The purpose of this module is to convert the speech waveform, using digital signal processing (DSP) tools, to a set of features (at a considerably lower information rate) for further analysis. This is often referred as the signal-processing front end.

The speech signal is a slowly timed varying signal (it is called quasi-stationary). An example of speech signal is shown in Figure 2. When examined over a sufficiently short period of time (between 5 and 100 msec), its characteristics are fairly stationary. However, over long periods of time (on the order of 1/5 seconds or more) the signal

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characteristic change to reflect the different speech sounds being spoken. Therefore, short-time spectral analysis is the most common way to characterize the speech signal.

Figure 2. Example of speech signal

A wide range of possibilities exist for parametrically representing the speech signal for the speaker recognition task, such as Linear Prediction Coding (LPC), Mel-Frequency Cepstrum Coefficients (MFCC), and others. MFCC is perhaps the best known and most popular, and will be described in this paper.

MFCC’s are based on the known variation of the human ear’s critical bandwidths with frequency, filters spaced linearly at low frequencies and logarithmically at high frequencies have been used to capture the phonetically important characteristics of speech. This is expressed in the mel-frequency scale, which is a linear frequency spacing below 1000 Hz and a logarithmic spacing above 1000 Hz. The process of computing MFCCs is described in more detail next.

2.2 Mel-frequency cepstrum coefficients processor

A block diagram of the structure of an MFCC processor is given in Figure 3. The speech input is typically recorded at a sampling rate above 10000 Hz. This sampling frequency was chosen to minimize the effects of aliasing in the analog-to-digital conversion. These sampled signals can capture all frequencies up to 5 kHz, which cover

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0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Time (second)

most energy of sounds that are generated by humans. As been discussed previously, the main purpose of the MFCC processor is to mimic the behavior of the human ears. In addition, rather than the speech waveforms themselves, MFFC’s are shown to be less susceptible to mentioned variations.

Figure 3. Block diagram of the MFCC processor

2.2.1 Frame Blocking

In this step the continuous speech signal is blocked into frames of N samples, with adjacent frames being separated by M (M < N). The first frame consists of the first N samples. The second frame begins M samples after the first frame, and overlaps it by N - M samples and so on. This process continues until all the speech is accounted for within one or more frames. Typical values for N and M are N = 256 (which is equivalent to ~ 30 msec windowing and facilitate the fast radix-2 FFT) and M = 100.

2.2.2 Windowing

The next step in the processing is to window each individual frame so as to minimize the signal discontinuities at the beginning and end of each frame. The concept here is to minimize the spectral distortion by using the window to taper the signal to zero at the beginning and end of each frame. If we define the window as , where N is the number of samples in each frame, then the result of windowing is the signal

Typically the Hamming window is used, which has the form:

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melcepstrum

melspectrum

framecontinuousspeech

FrameBlocking

Windowing FFT spectrum

Mel-frequencyWrapping

Cepstrum

2.2.3 Fast Fourier Transform (FFT)

The next processing step is the Fast Fourier Transform, which converts each frame of N samples from the time domain into the frequency domain. The FFT is a fast algorithm to implement the Discrete Fourier Transform (DFT), which is defined on the set of N samples {xn}, as follow:

In general Xk’s are complex numbers and we only consider their absolute values (frequency magnitudes). The resulting sequence {Xk} is interpreted as follow: positive frequencies correspond to values , while negative frequencies

correspond to . Here, Fs denotes the sampling frequency.

The result after this step is often referred to as spectrum or periodogram.

2.2.4 Mel-frequency Wrapping

As mentioned above, psychophysical studies have shown that human perception of the frequency contents of sounds for speech signals does not follow a linear scale. Thus for each tone with an actual frequency, f, measured in Hz, a subjective pitch is measured on a scale called the ‘mel’ scale. The mel-frequency scale is a linear frequency spacing below 1000 Hz and a logarithmic spacing above 1000 Hz.

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Figure 4. An example of mel-spaced filterbank

One approach to simulating the subjective spectrum is to use a filter bank, spaced uniformly on the mel-scale (see Figure 4). That filter bank has a triangular bandpass frequency response, and the spacing as well as the bandwidth is determined by a constant mel frequency interval. The number of mel spectrum coefficients, K, is typically chosen as 20. Note that this filter bank is applied in the frequency domain, thus it simply amounts to applying the triangle-shape windows as in the Figure 4 to the spectrum. A useful way of thinking about this mel-wrapping filter bank is to view each filter as a histogram bin (where bins have overlap) in the frequency domain.

2.2.5 Cepstrum

In this final step, we convert the log mel spectrum back to time. The result is called the mel frequency cepstrum coefficients (MFCC). The cepstral representation of the speech spectrum provides a good representation of the local spectral properties of the signal for the given frame analysis. Because the mel spectrum coefficients (and so their logarithm) are real numbers, we can convert them to the time domain using the Discrete Cosine Transform (DCT). Therefore if we denote those mel power spectrum coefficients that are the result of the last step are , we can calculate the MFCC's,

as

Note that we exclude the first component, from the DCT since it represents the mean value of the input signal, which carried little speaker specific information.

2.3 Summary

By applying the procedure described above, for each speech frame of around 30msec with overlap, a set of mel-frequency cepstrum coefficients is computed. These are result of a cosine transform of the logarithm of the short-term power spectrum expressed on a mel-frequency scale. This set of coefficients is called an acoustic vector. Therefore each input utterance is transformed into a sequence of acoustic vectors. In the next section we will see how those acoustic vectors can be used to represent and recognize the voice characteristic of the speaker.

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K-1nK

knScK

kkn ,...,1,0,

21cos)~(log~

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3 Feature Matching

3.1 Overview

The problem of speaker recognition belongs to a much broader topic in scientific and engineering so called pattern recognition. The goal of pattern recognition is to classify objects of interest into one of a number of categories or classes. The objects of interest are generically called patterns and in our case are sequences of acoustic vectors that are extracted from an input speech using the techniques described in the previous section. The classes here refer to individual speakers. Since the classification procedure in our case is applied on extracted features, it can be also referred to as feature matching.

Furthermore, if there exists some set of patterns that the individual classes of which are already known, then one has a problem in supervised pattern recognition. These patterns comprise the training set and are used to derive a classification algorithm. The remaining patterns are then used to test the classification algorithm; these patterns are collectively referred to as the test set. If the correct classes of the individual patterns in the test set are also known, then one can evaluate the performance of the algorithm.

The state-of-the-art in feature matching techniques used in speaker recognition include Dynamic Time Warping (DTW), Hidden Markov Modeling (HMM), and Vector Quantization (VQ). In this project, the VQ approach will be used, due to ease of implementation and high accuracy. VQ is a process of mapping vectors from a large vector space to a finite number of regions in that space. Each region is called a cluster and can be represented by its center called a codeword. The collection of all codewords is called a codebook.

Figure 5 shows a conceptual diagram to illustrate this recognition process. In the figure, only two speakers and two dimensions of the acoustic space are shown. The circles refer to the acoustic vectors from the speaker 1 while the triangles are from the speaker 2. In the training phase, using the clustering algorithm described in Section 4.2, a speaker-specific VQ codebook is generated for each known speaker by clustering his/her training acoustic vectors. The result codewords (centroids) are shown in Figure 5 by black circles and black triangles for speaker 1 and 2, respectively. The distance from a vector to the closest codeword of a codebook is called a VQ-distortion. In the recognition phase, an input utterance of an unknown voice is “vector-quantized” using each trained codebook and the total VQ distortion is computed. The speaker corresponding to the VQ codebook with smallest total distortion is identified as the speaker of the input utterance.

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Speaker 1

Speaker 1centroidsample

Speaker 2centroidsample

Speaker 2

VQ distortion

Figure 5. Conceptual diagram illustrating vector quantization codebook formation.One speaker can be discriminated from another based of the location of centroids.

3.2 Clustering the Training Vectors

After the enrolment session, the acoustic vectors extracted from input speech of each speaker provide a set of training vectors for that speaker. As described above, the next important step is to build a speaker-specific VQ codebook for each speaker using those training vectors. There is a well-know algorithm, namely LBG algorithm [Linde, Buzo and Gray, 1980], for clustering a set of L training vectors into a set of M codebook vectors. The algorithm is formally implemented by the following recursive procedure:

1. Design a 1-vector codebook; this is the centroid of the entire set of training vectors (hence, no iteration is required here).

2. Double the size of the codebook by splitting each current codebook yn according to the rule

where n varies from 1 to the current size of the codebook, and is a splitting parameter (we choose =0.01).

3. Nearest-Neighbor Search: for each training vector, find the codeword in the current codebook that is closest (in terms of similarity measurement), and assign that vector to the corresponding cell (associated with the closest codeword).

4. Centroid Update: update the codeword in each cell using the centroid of the training vectors assigned to that cell.

5. Iteration 1: repeat steps 3 and 4 until the average distance falls below a preset threshold

6. Iteration 2: repeat steps 2, 3 and 4 until a codebook size of M is designed.

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Intuitively, the LBG algorithm designs an M-vector codebook in stages. It starts first by designing a 1-vector codebook, then uses a splitting technique on the codewords to initialize the search for a 2-vector codebook, and continues the splitting process until the desired M-vector codebook is obtained.

Figure 6 shows, in a flow diagram, the detailed steps of the LBG algorithm. “Cluster vectors” is the nearest-neighbor search procedure which assigns each training vector to a cluster associated with the closest codeword. “Find centroids” is the centroid update procedure. “Compute D (distortion)” sums the distances of all training vectors in the nearest-neighbor search so as to determine whether the procedure has converged.

Figure 6. Flow diagram of the LBG algorithm

4 Project

As stated before, in this project we will experiment with the building and testing of an automatic speaker recognition system. In order to build such a system, one have to go through the steps that were described in previous sections. The most convenient platform for this is the Matlab environment since many of the above tasks were already implemented in Matlab. The project Web page given at the beginning provides a test database and several helper functions to ease the development process. We supplied you with two utility functions: melfb and disteu; and two main functions: train and test. Download all of these files from the project Web page into your working folder. The first two files can be treated as a black box, but the later two needs to be thoroughly understood. In fact, your tasks are to write two missing functions: mfcc and vqlbg, which will be called from the given main functions. In order to accomplish that, follow each

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step in this section carefully and check your understanding by answering all the questions.

4.1 Speech Data

Down load the ZIP file of the speech database from the project Web page. After unzipping the file correctly, you will find two folders, TRAIN and TEST, each contains 8 files, named: S1.WAV, S2.WAV, …, S8.WAV; each is labeled after the ID of the speaker. These files were recorded in Microsoft WAV format. In Windows systems, you can listen to the recorded sounds by double clicking into the files.

Our goal is to train a voice model (or more specific, a VQ codebook in the MFCC vector space) for each speaker S1 - S8 using the corresponding sound file in the TRAIN folder. After this training step, the system would have knowledge of the voice characteristic of each (known) speaker. Next, in the testing phase, the system will be able to identify the (assumed unknown) speaker of each sound file in the TEST folder.

4.2 Speech Processing

In this phase you are required to write a Matlab function that reads a sound file and turns it into a sequence of MFCC (acoustic vectors) using the speech processing steps described previously. Many of those tasks are already provided by either standard or our supplied Matlab functions. The Matlab functions that you would need are: wavread, hamming, fft, dct and melfb (supplied function). Type help function_name at the Matlab prompt for more information about these functions.

4.3 Vector Quantization

The result of the last section is that we transform speech signals into vectors in an acoustic space. In this section, we will apply the VQ-based pattern recognition technique to build speaker reference models from those vectors in the training phase and then can identify any sequences of acoustic vectors uttered by unknown speakers.

4.4 Simulation and Evaluation

Now is the final part! Use the two supplied programs: train and test (which require two functions mfcc and vqlbg that you just complete) to simulate the training and testing procedure in speaker recognition system, respectively.

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REFERENCES

[1] L.R. Rabiner and B.H. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, N.J., 1993.

[2] L.R Rabiner and R.W. Schafer, Digital Processing of Speech Signals, Prentice-Hall, Englewood Cliffs, N.J., 1978.

[3] S.B. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”, IEEE Transactions on Acoustics, Speech, Signal Processing, Vol. ASSP-28, No. 4, August 1980.

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PROGRAM%% Project: Voice Recognition and Identification system% By bukasa tshibangu, banzadio salazaku , mutumba maliro%--------------------------------------------------------------------------char st; char st1; char st2; char st3;disp('Project: Voice Recognition and Identification system');disp('By bukasa tshibangu & banzadio salazaku & mutumba maliro ');disp(' ');pause(0.5);disp('LOADING ');pause(1);disp('... ');pause(1);disp('... ');pause(1);disp('... ');pause(1);disp('... '); % Preallocating arraystr = {8}; fstr = {8}; nbtr = {8};ste = {8}; fste = {8}; nbte = {8};ctr = {8}; dtr={8};cte = {8}; dte={8};data = {drogba,4};code = {8}; for i = 1:8 % Read audio data from train folder for performing operations st=strcat('train\s',num2str(i),'.wav'); [s1 fs1 nb1]=wavread(st); str{i} = s1; fstr{i} = fs1; nbtr{i} = nb1; % Read audio data from test folder for performing operations st = strcat('test\s',num2str(i),'.wav'); [st1 fst1 nbt1] = wavread(st); ste{i} = st1; fste{i} = fst1; nbte{i} = nbt1;

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% Compute MFCC of the audio data to be used in Speech Processing for Train % Folder ctr{i} = mfcc(str{i},fstr{i}); % Compute MFCC of the audio data to be used in Speech Processing for Test % Folder cte{i} = mfcc(ste{i},fste{i}); % Compute Vector Quantization of the audio data to be used in Speech % Processing for Train Folder dtr{i} = vqlbg(ctr{i},16); % Compute Vector Quantization of the audio data to be used in Speech % Processing for Test Folder dte{i} = vqlbg(cte{i},16);end % For making Choicech=0;poss=11;while ch~=poss ch=menu('Speaker Recognition System','1: Human speaker recognition',... '2: Technical data of samples',... '3: Power Spectrum','4: Power Spectrum with different M and N',... '5: Mel-Spaced Filter Bank',... '6: Spectrum before and after Mel-Frequency wrapping',... '7: 2D plot of acoustic vectors',... '8: Plot of VQ codewords','9: Recognition rate of the computer',... '10: Test with other speech files','11: Exit'); disp(' '); %---------------------------------------------------------------------- %% 1: Human speaker recognition if ch==1 disp('> 1: Human speaker recognition'); disp('Play each sound file in the TRAIN folder.'); disp('Can you distinguish the voices of those eight speakers?'); disp('Now play each sound in the TEST folder in a random order without looking at the file name '); disp('and try to identify the speaker using your knowledge of their voices that you have just heard,'); disp('from the TRAIN folder. This is exactly what the computer will do in our system.'); disp(' '); disp(' ');

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disp('All of us seem to be unable to recognise random people just by listening to their voice. '); disp('We also realize that we do not identify speakers by the frequencies with which they use to talk, '); disp('but rather by other characteristics, like accent, speed, etc.'); pause(1); ch2=0; while ch2~=4 ch2=menu('Select Folder','Train','Test','User','Exit'); if ch2==1 ch3=0; while ch3~=9 ch3=menu('Train :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 p=audioplayer(str{ch3},fstr{ch3},nbtr{ch3}); play(p); end end end if ch2==2 ch3=0; while ch3~=9 ch3=menu('Test :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 p=audioplayer(ste{ch3},fste{ch3},nbte{ch3}); play(p); end end close all; end if ch2==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st};

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dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a = str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs nb]=wavread(st); p=audioplayer(s,fs,nb); play(p); else warndlg('Invalid Word ','Warning'); end end end close all; end else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 2: Technical data of samples if ch==2 disp('> 2: Technical data of samples'); ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss2=9; ch2=0; while ch2~=poss2 ch2=menu('Technical data of samples for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch2~=9

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t = 0:1/fstr{ch2}:(length(str{ch2}) - 1)/fstr{ch2}; plot(t, str{ch2}), axis([0, (length(str{ch2}) - 1)/fstr{ch2} -0.4 0.5]); st=sprintf('Plot of signal s%d.wav',ch2); title(st); xlabel('Time [s]'); ylabel('Amplitude (normalized)') end end close all end if ch23==2 poss2=9; ch2=0; while ch2~=poss2 ch2=menu('Technical data of samples for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch2~=9 t = 0:1/fste{ch2}:(length(ste{ch2}) - 1)/fste{ch2}; plot(t, ste{ch2}), axis([0, (length(ste{ch2}) - 1)/fste{ch2} -0.4 0.5]); st=sprintf('Plot of signal s%d.wav',ch2); title(st); xlabel('Time [s]'); ylabel('Amplitude (normalized)') end end close all end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def);

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an=cell2mat(an); a = str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st); t = 0:1/fs:(length(s) - 1)/fs; plot(t, s), axis([0, (length(s) - 1)/fs -0.4 0.5]); st=sprintf('Plot of signal %s',st); title(st); xlabel('Time [s]'); ylabel('Amplitude (normalized)') else warndlg('Invalid Word ','Warning'); end end end end close all; else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 3: linear and logarithmic power spectrum plot if ch==3 M = 100; N = 256; disp('> 3: Power Spectrum Plot'); disp(' '); disp('>Linear and Logarithmic spectrum plot'); ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Linear and Logarithmic Power Spectrum Plot for : ','Signal 1','Signal 2','Signal 3',...

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'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 % 3 (linear) frames = blockFrames(str{ch3}, fstr{ch3}, M, N); t = N / 2; tm = length(str{ch3}) / fstr{ch3}; subplot(121); imagesc([0 tm], [0 fstr{ch3}/2], abs(frames(1:t, :)).^2), axis xy; title('Power Spectrum (M = 100, N = 256)'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; % 3 (logarithmic) subplot(122); imagesc([0 tm], [0 fstr{ch3}/2], 20 * log10(abs(frames(1:t, :)).^2)), axis xy; title('Logarithmic Power Spectrum (M = 100, N = 256)'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; % D=get(gcf,'Position'); % set(gcf,'Position',round([D(1)*.5 D(2)*.5 D(3)*2 D(4)*1.3])) end end close all end if ch23==2 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Linear and Logarithmic Power Spectrum Plot for : ','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 % 3 (linear) frames = blockFrames(ste{ch3}, fste{ch3}, M, N); t = N / 2; tm = length(ste{ch3}) / fste{ch3}; subplot(121); imagesc([0 tm], [0 fste{ch3}/2], abs(frames(1:t, :)).^2), axis xy; title('Power Spectrum (M = 100, N = 256)'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; % 3 (logarithmic) subplot(122); imagesc([0 tm], [0 fste{ch3}/2], 20 * log10(abs(frames(1:t, :)).^2)), axis xy; title('Logarithmic Power Spectrum (M = 100, N = 256)');

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xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; % D=get(gcf,'Position'); % set(gcf,'Position',round([D(1)*.5 D(2)*.5 D(3)*2 D(4)*1.3])) end end close all; end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a = str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st); frames = blockFrames(s, fs, M, N); t = N / 2; tm = length(s) / fs; subplot(121); imagesc([0 tm], [0 fs/2], abs(frames(1:t, :)).^2), axis xy; title('Power Spectrum (M = 100, N = 256)'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; %Question 3 (logarithmic) subplot(122);

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imagesc([0 tm], [0 fs/2], 20 * log10(abs(frames(1:t, :)).^2)), axis xy; title('Logarithmic Power Spectrum (M = 100, N = 256)'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; else warndlg('Invalid Word ','Warning'); end end end end close all; else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 4: Plots for different values for N if ch==4 disp('> 4: Plots for different values for M and N'); lN = [128 256 512]; ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Plots for different values of M and N for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 u=220; for i = 1:length(lN) N = lN(i); M = round(N / 3); frames = blockFrames(str{ch3}, fstr{ch3}, M, N); t = N / 2; tm = length(str{ch3}) / fstr{ch3}; temp = size(frames); nbframes = temp(2); u=u+1; subplot(u)

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imagesc([0 tm], [0 fstr{ch3}/2], 20 * log10(abs(frames(1:t, :)).^2)), axis xy; title(sprintf('Power Spectrum (M = %i, N = %i, frames = %i)', M, N, nbframes)); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar end % D=get(gcf,'Position'); % set(gcf,'Position',round([D(1)*.5 D(2)*.5 D(3)*1.5 D(4)*1.5])) end end close all end if ch23==2 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Plots for different values of M and N for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 u=220; for i = 1:length(lN) N = lN(i); M = round(N / 3); frames = blockFrames(ste{ch3}, fste{ch3}, M, N); t = N / 2; tm = length(ste{ch3}) / fste{ch3}; temp = size(frames); nbframes = temp(2); u=u+1; subplot(u) imagesc([0 tm], [0 fste{ch3}/2], 20 * log10(abs(frames(1:t, :)).^2)), axis xy; title(sprintf('Power Spectrum (M = %i, N = %i, frames = %i)', M, N, nbframes)); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar end % D=get(gcf,'Position'); % set(gcf,'Position',round([D(1)*.5 D(2)*.5 D(3)*1.5 D(4)*1.5])) end end close all; end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat');

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ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a = str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st); u=220; for i = 1:length(lN) N = lN(i); M = round(N / 3); frames = blockFrames(s, fs, M, N); t = N / 2; tm = length(s) / fs; temp = size(frames); nbframes = temp(2); u=u+1; subplot(u) imagesc([0 tm], [0 fs/2], 20 * log10(abs(frames(1:t, :)).^2)), axis xy; title(sprintf('Power Spectrum (M = %i, N = %i, frames = %i)', M, N, nbframes)); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar end else warndlg('Invalid Word ','Warning'); end end end end close all; else

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warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 5: Mel Space if ch==5 disp('> 5: Mel Space'); disp(' '); disp('Mel Space is function of sampling rate and since all signals '); disp('are recorded at same sampling rate so they have same Mel Space.'); ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Mel Space for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 plot(linspace(0, (fstr{ch3}/2), 129), (melfb(20, 256, fstr{ch3}))); title('Mel-Spaced Filterbank'); xlabel('Frequency [Hz]'); end end close all end if ch23==2 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Mel Space for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 plot(linspace(0, (fste{ch3}/2), 129), (melfb(20, 256, fste{ch3}))); title('Mel-Spaced Filterbank'); xlabel('Frequency [Hz]'); end end close all;

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end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a=str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st); plot(linspace(0, (fs/2), 129), (melfb(20, 256, fs))); title('Mel-Spaced Filterbank'); xlabel('Frequency [Hz]'); else warndlg('Invalid Word ','Warning'); end end end end close all; else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 6: Modified spectrum

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if ch==6 disp('> 6: Modified spectrum'); disp(' '); disp('Spectrum before and after Mel-Frequency wrapping'); M = 100; N = 256; n2 = 1 + floor(N / 2); ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Mel Space for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 frames = blockFrames(str{ch3}, fstr{ch3}, M, N); m = melfb(20, N, fstr{ch3}); z = m * abs(frames(1:n2, :)).^2; tm = length(str{ch3}) / fstr{ch3}; subplot(121) imagesc([0 tm], [0 fstr{ch3}/2], abs(frames(1:n2, :)).^2), axis xy; title('Power Spectrum unmodified'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; subplot(122) imagesc([0 tm], [0 20], z), axis xy; title('Power Spectrum modified through Mel Cepstrum filter'); xlabel('Time [s]'); ylabel('Number of Filter in Filter Bank'); % colorbar;D=get(gcf,'Position'); % set(gcf,'Position',[0 D(2) D(3)/2 D(4)]) end end close all end if ch23==2 poss3=9; ch3=0; while ch3~=poss3 ch3=menu('Mel Space for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch3~=9 frames = blockFrames(str{ch3}, fstr{ch3}, M, N);

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m = melfb(20, N, fstr{ch3}); z = m * abs(frames(1:n2, :)).^2; tm = length(str{ch3}) / fstr{ch3}; subplot(121) imagesc([0 tm], [0 fstr{ch3}/2], abs(frames(1:n2, :)).^2), axis xy; title('Power Spectrum unmodified'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; subplot(122) imagesc([0 tm], [0 20], z), axis xy; title('Power Spectrum modified through Mel Cepstrum filter'); xlabel('Time [s]'); ylabel('Number of Filter in Filter Bank'); % colorbar;D=get(gcf,'Position'); % set(gcf,'Position',[0 D(2) D(3)/2 D(4)]) end end close all; end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a=str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st); frames = blockFrames(s, fs, M, N); m = melfb(20, N, fs);

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z = m * abs(frames(1:n2, :)).^2; tm = length(s) / fs; subplot(121) imagesc([0 tm], [0 fs/2], abs(frames(1:n2, :)).^2), axis xy; title('Power Spectrum unmodified'); xlabel('Time [s]'); ylabel('Frequency [Hz]'); colorbar; subplot(122) imagesc([0 tm], [0 20], z), axis xy; title('Power Spectrum modified through Mel Cepstrum filter'); xlabel('Time [s]'); ylabel('Number of Filter in Filter Bank'); colorbar; else warndlg('Invalid Word ','Warning'); end end end end close all; else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 7: 2D plot of accustic vectors if ch==7 disp('> 7: 2D plot of accustic vectors'); ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss3=3; ch3=0; while ch3~=poss3 ch3=menu('2D plot of accustic vectors representation : ','1. One Signal',... '2. Two Signal','3. Exit'); if ch3==1 ch31=0; while ch31~=9 ch31=menu('2D plot of accustic vectors for :','Signal 1','Signal 2','Signal 3',...

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'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch31~=9 plot(ctr{ch31}(5, :), ctr{ch31}(6, :), 'or'); xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Signal %d ',ch31); legend(st); title('2D plot of accoustic vectors'); end end close all; end if ch3==2 ch32=0; while ch32~=8 ch32=menu('2D plot of accustic vectors for :','Signal 1 & Signal 2',... 'Signal 2 & Signal 3','Signal 3 & Signal 4','Signal 4 & Signal 5',... 'Signal 5 & Signal 6','Signal 6 & Signal 7','Signal 7 & Signal 8','Exit'); if ch32~=8 plot(ctr{ch32}(5, :), ctr{ch32}(6, :), 'or'); hold on; plot(ctr{ch32+1}(5, :), ctr{ch32+1}(6, :), 'xb'); xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Signal %d,',ch32); st1=sprintf('Signal %d', (ch32+1) ); legend(st,st1); title('2D plot of accoustic vectors'); hold off end end end close all end end if ch23==2 poss3=3; ch3=0; while ch3~=poss3 ch3=menu('2D plot of accustic vectors representation : ','1. One Signal',... '2. Two Signal','3. Exit'); if ch3==1 ch31=0; while ch31~=9 ch31=menu('2D plot of accustic vectors for :','Signal 1','Signal 2','Signal 3',...

30

'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch31~=9 plot(cte{ch31}(5, :), cte{ch31}(6, :), 'or'); xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Signal %d ',ch31); legend(st); title('2D plot of accoustic vectors'); end end close all; end if ch3==2 ch32=0; while ch32~=8 ch32=menu('2D plot of accustic vectors for :','Signal 1 & Signal 2',... 'Signal 2 & Signal 3','Signal 3 & Signal 4','Signal 4 & Signal 5',... 'Signal 5 & Signal 6','Signal 6 & Signal 7','Signal 7 & Signal 8','Exit'); if ch32~=8 plot(cte{ch32}(5, :), cte{ch32}(6, :), 'or'); hold on; plot(cte{ch32+1}(5, :), cte{ch32+1}(6, :), 'xb'); xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Signal %d,',ch32); st1=sprintf('Signal %d', (ch32+1) ); legend(st,st1); title('2D plot of accoustic vectors'); hold off end end end close all end end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1

31

st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a=str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st); c = mfcc(s, fs); plot(c(5, :), c(6, :), 'or'); xlabel('5th Dimension'); ylabel('6th Dimension'); st1=sprintf('Signal %s.wav',st); legend(st1); title('2D plot of accoustic vectors'); else warndlg('Invalid Word ','Warning'); end end end end close all; else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 8: Plot of the 2D trained VQ codewords if ch==8 disp('> 8: Plot of the 2D trained VQ codewords'); ch23=0; while ch23~=4 ch23=menu('Select Folder','Train','Test','User','Exit'); if ch23==1 poss3=3;

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ch3=0; while ch3~=poss3 ch3=menu('2D plot of accustic vectors representation : ','1. One Signal',... '2. Two Signal','3. Exit'); if ch3==1 ch31=0; while ch31~=9 ch31=menu('2D plot of accustic vectors for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch31~=9 plot(ctr{ch31}(5, :), ctr{ch31}(6, :), 'xr') hold on plot(dtr{ch31}(5, :), dtr{ch31}(6, :), 'vk') xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Speaker %d',ch31); st1=sprintf('Codebook %d', (ch31) ); legend(st,st1); title('2D plot of accoustic vectors'); hold off end end close all end if ch3==2 ch32=0; while ch32~=8 ch32=menu('2D plot of accustic vectors for :','Signal 1 & Signal 2',... 'Signal 2 & Signal 3','Signal 3 & Signal 4','Signal 4 & Signal 5',... 'Signal 5 & Signal 6','Signal 6 & Signal 7','Signal 7 & Signal 8','Exit'); if ch32~=8 plot(ctr{ch32}(5, :), ctr{ch32}(6, :), 'xr') hold on plot(dtr{ch32}(5, :), dtr{ch32}(6, :), 'vk') plot(ctr{ch32+1}(5, :), ctr{ch32+1}(6, :), 'xb') plot(dtr{ch32+1}(5, :), dtr{ch32+1}(6, :), '+k') xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Speaker %d',ch32); st1=sprintf('Codebook %d',ch32 ); st2=sprintf('Speaker %d',(ch32+1) );

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st3=sprintf('Codebook %d', (ch32+1) ); legend(st,st1,st2,st3); title('2D plot of accoustic vectors'); hold off end end end close all end end if ch23==2 poss3=3; ch3=0; while ch3~=poss3 ch3=menu('2D plot of accustic vectors representation : ','1. One Signal',... '2. Two Signal','3. Exit'); if ch3==1 ch31=0; while ch31~=9 ch31=menu('2D plot of accustic vectors for :','Signal 1','Signal 2','Signal 3',... 'Signal 4','Signal 5','Signal 6','Signal 7','Signal 8','Exit'); if ch31~=9 plot(cte{ch31}(5, :), cte{ch31}(6, :), 'xr') hold on plot(dte{ch31}(5, :), dte{ch31}(6, :), 'vk') xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Speaker %d',ch31); st1=sprintf('Codebook %d', (ch31) ); legend(st,st1); title('2D plot of accoustic vectors'); hold off end end close all end if ch3==2 ch32=0; while ch32~=8 ch32=menu('2D plot of accustic vectors for :','Signal 1 & Signal 2',... 'Signal 2 & Signal 3','Signal 3 & Signal 4','Signal 4 & Signal 5',... 'Signal 5 & Signal 6','Signal 6 & Signal 7','Signal 7 & Signal 8','Exit'); if ch32~=8

34

plot(cte{ch32}(5, :), cte{ch32}(6, :), 'xr') hold on plot(dte{ch32}(5, :), dte{ch32}(6, :), 'vk') plot(cte{ch32+1}(5, :), cte{ch32+1}(6, :), 'xb') plot(dte{ch32+1}(5, :), dte{ch32+1}(6, :), '+k') xlabel('5th Dimension'); ylabel('6th Dimension'); st=sprintf('Speaker %d',ch32); st1=sprintf('Codebook %d',ch32 ); st2=sprintf('Speaker %d', (ch32+1) ); st3=sprintf('Codebook %d', (ch32+1) ); legend(st,st1,st2,st3); title('2D plot of accoustic vectors'); hold off end end end close all end end if ch23==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a=str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs]=wavread(st);

35

c = mfcc(s, fs); d = vqlbg(c, 16); plot(c(5, :), c(6, :), 'xr'); hold on plot(d(5, :), d(6, :), 'vk'); xlabel('5th Dimension'); ylabel('6th Dimension'); st1=sprintf('Speaker %s',st); st2=sprintf('Codebook %s',st); legend(st1,st2); title('2D plot of accoustic vectors'); hold off else warndlg('Invalid Word ','Warning'); end end end end close all; else warndlg('Database is empty.',' Warning ') end end end end %---------------------------------------------------------------------- %% 9: Recognition rate of the computer if ch==9 disp('> 9: Recognition rate of the computer') %---------------------------------------------------------------------- %% 9.1 Loading from Test Folder for Comparison% All 8 samples data values are loaded in file sounddatabase1.dat. for sound_number = 1: 8 if size(ste{sound_number},2)==2 ste{sound_number}=ste{sound_number}(:,1); end ste{sound_number} = double( ste{sound_number} ); data{sound_number,1} = ste{sound_number}; data{sound_number,2} = sound_number; st = sprintf('s%d.wav',sound_number); data{sound_number,3} = st; data{sound_number,4} = 'Test'; fs=fste{sound_number}; %#ok<NASGU> nb=nbte{sound_number}; %#ok<NASGU> if sound_number == 1; save('sound_database1.dat','data','sound_number','fs','nb'); else

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save('sound_database1.dat','data','sound_number','fs','nb','-append'); end end disp(' '); disp('Sounds From TEST added to database for comparison'); disp(' '); %---------------------------------------------------------------------- %% 9.2 Comparing one by one data from TRAIN FOLDER disp('Comparing one by one data from TRAIN FOLDER'); disp(' '); load('sound_database1.dat','-mat'); k =16; for ii=1:sound_number % Compute MFCC cofficients for each sound present in database v = mfcc(data{ii,1}, fstr{ii}); % Train VQ codebook code{ii} = vqlbg(v, k); end flag1 = 0; for classe = 1:8 st = sprintf('Train\\S%d.wav to be compared',classe); disp(st); pause(0.5); if size(str{classe},2)==2 str{classe}=str{classe}(:,1); end str{classe} = double(str{classe}); %----- code for speaker recognition ------- disp('MFCC cofficients computation and VQ codebook training in progress...'); % Number of centroids required disp(' '); % Compute MFCC coefficients for input sound v = mfcc(str{classe},fstr{classe}); % Current distance and sound ID initialization distmin = Inf; k1 = 0; for ii=1:sound_number d = disteu(v, code{ii}); dist = sum(min(d,[],2)) / size(d,1); if dist < distmin distmin = dist; k1 = ii; end end min_index = k1; speech_id = data{min_index,2}; %----------------------------------------- disp('Completed.'); disp('Matching sound:'); disp(' ');

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message=strcat('File:',data{min_index,3}); disp(message); message=strcat('Location:',data{min_index,4}); disp(message); message = strcat('Recognized speaker ID: ',num2str(speech_id)); disp(message); disp(' '); if classe == speech_id flag1 = flag1 + 1; end end disp(' '); pause(0.5) st1 = strcat('This prototype is', num2str(100*flag1/classe),'% efficient in recognising these 8 different stored sounds in TEST and TRAIN folders.'); msgbox(st1,'Success','help'); end %--------------------------------------------------------------------- if ch==10 disp('> 10: Test with other speech files') msgbox('P.S. This prototype is for secondary security usage.','NOTE','help'); pause(2); msgbox('Kindly Note this works for the stored databases only. This means that you can add sounds to the database by users and Recognition will be done for the users entered. ','NOTE','help') pause(2); chos=0; possibility=5; while chos~=possibility, chos=menu('Speaker Recognition System','Add a new sound from microphone',... 'Speaker recognition from microphone',... 'Database Info','Delete database','Exit'); %---------------------------------------------------------------------- %% 10.1 Add a new sound from microphone if chos==1 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); classe = input('Insert a class number (sound ID) that will be used for recognition:'); if isempty(classe) classe = sound_number+1; disp( num2str(classe) ); end

38

message=('The following parameters will be used during recording:'); disp(message); message=strcat('Sampling frequency',num2str(samplingfrequency)); disp(message); message=strcat('Bits per sample',num2str(samplingbits)); disp(message); durata = input('Insert the duration of the recording (in seconds):'); if isempty(durata) durata = 3; disp( num2str(durata) ); end micrecorder = audiorecorder(samplingfrequency,samplingbits,1); disp('Now, speak into microphone...'); record(micrecorder,durata); while (isrecording(micrecorder)==1) disp('Recording...'); pause(0.5); end disp('Recording stopped.'); y1 = getaudiodata(micrecorder); y = getaudiodata(micrecorder, 'uint8'); if size(y,2)==2 y=y(:,1); end y = double(y); sound_number = sound_number+1; data{sound_number,1} = y; data{sound_number,2} = classe; data{sound_number,3} = 'Microphone'; data{sound_number,4} = 'Microphone'; st=strcat('u',num2str(sound_number)); wavwrite(y1,samplingfrequency,samplingbits,st) save('sound_database.dat','data','sound_number','-append'); msgbox('Sound added to database','Database result','help'); disp('Sound added to database'); else classe = input('Insert a class number (sound ID) that will be used for recognition:'); if isempty(classe) classe = 1; disp( num2str(classe) ); end durata = input('Insert the duration of the recording (in seconds):'); if isempty(durata) durata = 3; disp( num2str(durata) );

39

end samplingfrequency = input('Insert the sampling frequency (22050 recommended):'); if isempty(samplingfrequency ) samplingfrequency = 22050; disp( num2str(samplingfrequency) ); end samplingbits = input('Insert the number of bits per sample (8 recommended):'); if isempty(samplingbits ) samplingbits = 8; disp( num2str(samplingbits) ); end micrecorder = audiorecorder(samplingfrequency,samplingbits,1); disp('Now, speak into microphone...'); record(micrecorder,durata); while (isrecording(micrecorder)==1) disp('Recording...'); pause(0.5); end disp('Recording stopped.'); y1 = getaudiodata(micrecorder); y = getaudiodata(micrecorder, 'uint8'); if size(y,2)==2 y=y(:,1); end y = double(y); sound_number = 1; data{sound_number,1} = y; data{sound_number,2} = classe; data{sound_number,3} = 'Microphone'; data{sound_number,4} = 'Microphone'; st=strcat('u',num2str(sound_number)); wavwrite(y1,samplingfrequency,samplingbits,st) save('sound_database.dat','data','sound_number','samplingfrequency','samplingbits'); msgbox('Sound added to database','Database result','help'); disp('Sound added to database'); end end %---------------------------------------------------------------------- %% 10.2 Voice Recognition from microphone if chos==2 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); Fs = samplingfrequency; durata = input('Insert the duration of the recording (in seconds):');

40

if isempty(durata) durata = 3; disp( num2str(durata) ); end micrecorder = audiorecorder(samplingfrequency,samplingbits,1); disp('Now, speak into microphone...'); record(micrecorder,durata); while (isrecording(micrecorder)==1) disp('Recording...'); pause(0.5); end disp('Recording stopped.'); y = getaudiodata(micrecorder); st='v'; wavwrite(y,samplingfrequency,samplingbits,st); y = getaudiodata(micrecorder, 'uint8'); % if the input sound is not mono if size(y,2)==2 y=y(:,1); end y = double(y); %----- code for speaker recognition ------- disp('MFCC cofficients computation and VQ codebook training in progress...'); disp(' '); % Number of centroids required k =16; for ii=1:sound_number % Compute MFCC cofficients for each sound present in database v = mfcc(data{ii,1}, Fs); % Train VQ codebook code{ii} = vqlbg(v, k); disp('...'); end disp('Completed.'); % Compute MFCC coefficients for input sound v = mfcc(y,Fs); % Current distance and sound ID initialization distmin = Inf; k1 = 0; for ii=1:sound_number d = disteu(v, code{ii}); dist = sum(min(d,[],2)) / size(d,1); message=strcat('For User #',num2str(ii),' Dist : ',num2str(dist)); disp(message); if dist < distmin distmin = dist; k1 = ii; end

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end if distmin < ronaldo min_index = k1; speech_id = data{min_index,2}; %----------------------------------------- disp('Matching sound:'); message=strcat('File:',data{min_index,3}); disp(message); message=strcat('Location:',data{min_index,4}); disp(message); message = strcat('Recognized speaker ID: ',num2str(speech_id)); disp(message); msgbox(message,'Matching result','help'); ch3=0; while ch3~=3 ch3=menu('Matched result verification:','Recognized Sound','Recorded sound','Exit'); if ch3==1 st=strcat('u',num2str(speech_id)); [s fs nb]=wavread(st); p=audioplayer(s,fs,nb); play(p); end if ch3==2 [s fs nb]=wavread('v'); p=audioplayer(s,fs,nb); play(p); end end else warndlg('Wrong User . No matching Result.',' Warning ') end else warndlg('Database is empty. No matching is possible.',' Warning ') end end %---------------------------------------------------------------------- %% 10.3 Database Info if chos==3 if (exist('sound_database.dat','file')==2) load('sound_database.dat','-mat'); message=strcat('Database has #',num2str(sound_number),'words:'); disp(message); disp(' ');

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for ii=1:sound_number message=strcat('Location:',data{ii,3}); disp(message); message=strcat('File:',data{ii,4}); disp(message); message=strcat('Sound ID:',num2str(data{ii,2})); disp(message); disp('-'); end ch32=0; while ch32 ~=2 ch32=menu('Database Information','Database','Exit'); if ch32==1 st=strcat('Sound Database has : #',num2str(sound_number),'words. Enter a database number : #'); prompt = {st}; dlg_title = 'Database Information'; num_lines = 1; def = {'1'}; options.Resize='on'; options.WindowStyle='normal'; options.Interpreter='tex'; an = inputdlg(prompt,dlg_title,num_lines,def); an=cell2mat(an); a=str2double(an); if (isempty(an)) else if (a <= sound_number) st=strcat('u',num2str(an)); [s fs nb]=wavread(st); p=audioplayer(s,fs,nb); play(p); else warndlg('Invalid Word ','Warning'); end end end end else warndlg('Database is empty.',' Warning ') end end %---------------------------------------------------------------------- %% 10.4 Delete database if chos==4

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%clc; close all; if (exist('sound_database.dat','file')==2) button = questdlg('Do you really want to remove the Database?'); if strcmp(button,'Yes') load('sound_database.dat','-mat'); for ii=1:sound_number st=strcat('u',num2str(ii),'.wav'); delete(st); end if (exist('v.wav','file')==2) delete('v.wav'); end delete('sound_database.dat'); msgbox('Database was succesfully removed from the current directory.','Database removed','help'); end else warndlg('Database is empty.',' Warning ') end end end end endclose all;msgbox('Kindly motivate our efforts. Feel free to provide valuable feedback.','Thank You','help');end%-------------------------------------------------------------------------- function M3 = blockFrames(s, fs, m, n)l = length(s);nbFrame = floor((l - n) / m) + 1;for i = 1:n for j = 1:nbFrame M(i, j) = s(((j - 1) * m) + i); %#ok<AGROW> endendh = hamming(n);M2 = diag(h) * M;for i = 1:nbFrame M3(:, i) = fft(M2(:, i)); %#ok<AGROW>endend%--------------------------------------------------------------------------

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%% MFCC Functionfunction r = mfcc(s, fs)m = 100;n = 256;frame=blockFrames(s, fs, m, n);m = melfb(20, n, fs);n2 = 1 + floor(n / 2);z = m * abs(frame(1:n2, :)).^2;r = dct(log(z));end%--------------------------------------------------------------------------function r = vqlbg(d,k)e = .01;r = mean(d, 2);dpr = 10000;for i = 1:log2(k) r = [r*(1+e), r*(1-e)]; while (1 == 1) z = disteu(d, r); [m,ind] = min(z, [], 2); t = 0; for j = 1:2^i r(:, j) = mean(d(:, find(ind == j)), 2); %#ok<FNDSB> x = disteu(d(:, find(ind == j)), r(:, j)); %#ok<FNDSB> for q = 1:length(x) t = t + x(q); end end if (((dpr - t)/t) < e) break; else dpr = t; end endendend%--------------------------------------------------------------------------

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