1. Speech/Audio Signal Processing in MATLAB/Simulink
2018/11/17
2006 Speech/Audio Signal Processing in MATLAB/Simulink
Speech/Audio Signal Processing in MATLAB/Simulink
J.-S. Roger Jang (張智星)
CS Dept, Tsing-Hua Univ, Taiwan
(清華大學 資訊系)
...
In this talk, we are going to apply two neural network controller design techniques to fuzzy controllers, and construct the so-called on-line adaptive neuro-fuzzy controllers for nonlinear control systems. We are going to use MATLAB, SIMULINK and Handle Graphics to demonstrate the concept. So you can also get a preview of some of the features of the Fuzzy Logic Toolbox, or FLT, version 2.

2. Outline Wave file manipulation Time-domain processing
2018/11/17
Outline
Wave file manipulation
Reading, writing, recording ...
Time-domain processing
Delay, filtering, sptools …
Frequency-domain processing
Spectrogram
Pitch determination
Auto-correlation, SIFT, AMDF, HPS ...
Others
Formant estimation, speech coding
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

3. Toolbox/Blockset Used
2018/11/17
Toolbox/Blockset Used
MATLAB
Simulink
Signal Processing Toolbox
DSP Blockset
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

4. 2018/11/17
MATLAB Primer
Before you start, you need to get familiar with MATLAB. Please read “MATLAB Primer” at the following page:
Exercise:
Please plot two curves y=sin(2*t) and y=cos(3*t) in the same figure.
Please plot x vs. y where x=sin(2*t) and y=cos(3*t).
2018/11/17

5. To Read a Wave File To read a MS .wav file (PCM format only): wavread
2018/11/17
To Read a Wave File
To read a MS .wav file (PCM format only): wavread
y = wavread(file)
[…] = wavread(file, [n1, n2])
[y, fs, nbits, opts] = wavread(file)
[…] = wavread(file, n)
[y, fs, nbits] = wavread(file)
If the wav file is stereo, y will be a two-column matrix.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

6. To Read a Wav File Example (wavRead01.m): Exercise：
2018/11/17
To Read a Wav File
Example (wavRead01.m):
[y, fs] = wavread('singapore.wav');
plot((1:length(y))/fs, y);
xlabel('Time in seconds');
ylabel('Amplitude');
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
Exercise：
Plot the waveform of “rrrrr.wav”. Use MATLAB’s “zoom” button to find the consecutive curling “R” occurs.
Plot the two-channel waveform in “flanger.wav”.
2018/11/17

7. Solution to the Previous Exercise
2018/11/17
Solution to the Previous Exercise
wavRead02.m:
[y, fs] = wavread(‘flanger.wav’);
subplot(2,1,1), plot((1:length(y))/fs, y(:,1));
subplot(2,1,2), plot((1:length(y))/fs, y(:,2));
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8. 2018/11/17
To Play Wav Files
To play sound using Windows audio output device: wavplay, sound, soundsc
wavplay(y, fs)
wavplay(y, fs, ‘async’): non-blocking call
wavplay(y, fs, ‘sync’): blocking call
sound(y, fs)
soundsc(…): autoscale the sound
Example (wavPlay01.m)：
[y, fs] = wavread(‘rrrrr.wav’);
wavplay(y, fs);
Exercise：
Follow the example to play “flanger.wav”.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

9. To Read/Play Using DSP Blocks
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To Read/Play Using DSP Blocks
To read/play sound using DSP Blockset:
DSP Blockset/DSP Sources/From Wave File
DSP Blockset/DSP Sinks/To Wave Device
Example:
Exercise:
Create a model as shown above.
The file name is “waveio.mdl”.
Frame-based operation!
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10. Solution Solution to the previous exercise: slWavFilePlay01.mdl
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Solution
Solution to the previous exercise:
slWavFilePlay01.mdl
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11. To Write a Wave File To write MS wave files: wavwrite
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To Write a Wave File
To write MS wave files: wavwrite
wavwrite(y, fs, nbits, wavefile)
“nbits” must be 8 or 16.
“y” must have two columns for stereo data.
Amplitude values outside [-1,1] are clipped.
Example (wavWrite01.m)：
[y, fs] = wavread(‘rrrrr.wav’);
wavwrite(y, fs*1.2, 8, ‘testout.wav’);
!start testout.wav
Exercise：
Try out the above example.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

12. To Record a Wave File To record wave files: Example： Exercise:
2018/11/17
To Record a Wave File
To record wave files:
1. Use the recording utility under WinXP.
2. Use “wavrecord” under MATLAB.
3. Use “From Wave Device” under Simulink, under “DSP Blocksets/Platform Specific IO/Windows (Win32)”
Example：
1. Go ahead and try WinXP recording utility!
2. Try “wavRecord01.m”
3. Try “slWavFileRecord01.mdl”
Exercise:
Try out the above examples.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
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13. Time-Domain Speech Signals
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Time-Domain Speech Signals
A typical time-domain plot of speech signals:
Amplitude: volume or intensity
Frequency: pitch
To generate the plot, try “plotwave.m”.
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14. Changing Wave Playback Param.
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Changing Wave Playback Param.
To control the play of a sound:
Normal: wavplay(y, fs)
High volume: wavplay(2*y, fs)
Low volume: wavplay(0.5*y, fs)
High pitch (and faster): wavplay(y, 1.2*fs)
Low pitch (and slower): wavplay(y, 0.8*fs)
Exercise:
Try “wavPlay01.m” and trace the code.
Create “wavPlay02.m” such that you can record your own voice on the fly.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

15. Time-Domain Signal Processing
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Time-Domain Signal Processing
Take-home exrecise:
How to get a high pitch with the same time span?
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

16. 2018/11/17
Synthetic Sounds
Use a sine wave generator (under DSP blocksets) to produce sounds
Single frequency:
Multiple frequencies:
Amplitude modulation:
Exercise:
Create the above models.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

17. Solution Solution to the previous exercise: sineSource01 sineSource02
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Solution
Solution to the previous exercise:
sineSource01
sineSource02
sineSource03
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18. Delay in Speech/Audio What is a delay in a signal?
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Delay in Speech/Audio
What is a delay in a signal?
y(n) --> y(n-k)
What effects can delay generate?
Echo
Reverberation
Chorus
Flanging
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
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19. Single Delay in Audio Signal
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Single Delay in Audio Signal
Block diagram: z -k
Input a
Output
u(n)
y(n) =
u(n) + a*u(n-k)
Simulink model:
The file is “delay1.mdl”.
Exercise:
Create the above model.
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20. Multiple Delay in Audio Signal
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Multiple Delay in Audio Signal
How to create “karaoke” effects: a z -k
Input
Output y(n)
u(n) 2 3
y(n) = u(n) + a u(n-k) + a u(n-2k) + a u(n-3k) ...
Delay2.mdl
Simulink model:
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21. Multiple Delay in Audio Signal
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Multiple Delay in Audio Signal
Parameter values:
Feedback gain a < 1
Actual delay time = k/fs
Exercise:
Create the above model and change some parameters to see their effects.
Modify the model to take microphone input (so you can start singing karaoke now!)
Use a “configurable subsystem” to include all possible input files and the microphone. (See next page.)
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
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22. Multiple Delay in Audio Signal
2018/11/17
Multiple Delay in Audio Signal
How to use “configurable subsystem” block?
1. Create a library (say, wavinput.mdl)
2. Get a block of “configurable subsystem”
3. Fill the dialog box with the library name
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

23. Audio Flanging Flanging sound: Simulink demo:
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Audio Flanging
Flanging sound:
A sound similar to the sound of a jet plane flying overhead, or a "whooshing" sound
“Pitch modulation” due to a variable delay
Simulink demo:
dspafxf.mdl (all platforms)
dspafxf_nt.mdl (for 95/98/NT)
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

24. Original spectrogram: Modified spectrogram:
2018/11/17
Audio Flanging
Simulink model:
Original spectrogram:
Modified spectrogram:
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

25. Signal Processing Using sptool
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Signal Processing Using sptool
To invoke sptool, type “sptool”.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

26. Speech Production How is speech produced? Analogy to System Theory:
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Speech Production
How is speech produced?
Speech is produced when air is forced from the lungs through the vocal cords (glottis) and along the vocal tract.
Analogy to System Theory:
Input: air forced into the vocal cords
Output: media vibration
System (or filter): vocal tract
Pitch frequency: frequency of the input
Formant frequency: resonant frequency
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

27. Source Filter Model of Speech
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Source Filter Model of Speech
The source-filter model of speech production:
Speech is split into a rapidly varying excitation signal and a slowly varying filter. The envelope of the power spectra contains the vocal tract information.
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
Two important characteristics of the model are fundamental (pitch) frequency (f0) and formants (F1, F2, F3, …)
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28. Frame Analysis of Speech Signal
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Frame Analysis of Speech Signal
Speech wave form :
Zoom in
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
Overlap
Frame
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29. 2018/11/17
Spectrogram
Spectrogram (specgram.m) displays short-time frequency contents:
Wave form :
Spectrogram :
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30. Real-time Spectrogram
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Real-time Spectrogram
Try “dspstfft_win32”:
Spectrum:
Spectrogram:
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31. Pitch and Formants Pitch and formants can be defined visually:
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Pitch and Formants
Pitch and formants can be defined visually:
Pitch period = 1/f0
First formant F1
Second formant F2
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32. Spectrogram Reading Spectrogram Reading
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Spectrogram Reading
Spectrogram Reading
Waveform:
Spectrogram:
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“compute”

33. Pitch Determination Algorithms
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Pitch Determination Algorithms
Time-domain:
Auto-correlation
AMDF (Average Magnitude Difference Function)
Gold-Rabiner algorithm (1969)
Frequency-domain:
Cepstrum (Noll 1964)
Harmonic product spectrum (Schroeder 1968)
Others:
SIFT (Simple inverse filter tracking)
Maximum likelihood
Neural network approach
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

34. Autocorrelation of Each Frame
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Autocorrelation of Each Frame
Let s(k) be a frame of size 128. 1
128
s(k):
s(k-h):
h=30
x(30) = dot prod. of overlapped
= sum(s(31:128).*s(1:99)
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
Autocorrelation
x(h):
2018/11/17
Pitch period 30

35. Autocorrelation via DSP Blockset
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Autocorrelation via DSP Blockset
Real-time autocorrelation demo:
Exercise:
Construct the above model and try it.
File name pitch1.mdl
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36. Pitch Tracking via Autocorrelation
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Pitch Tracking via Autocorrelation
Real-time pitch tracking via autocorrelation: pitch2.mdl
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

37. Formant Analysis Characteristics of formants: Computation methods:
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Formant Analysis
Characteristics of formants:
Formants are perceptually defined.
The corresponding physical property is the frequencies of resonances of the vocal tract.
Formant analysis is useful as the position of the first two formants pretty much identifies a vowel.
Computation methods:
Peak picking on the smoothed spectrum
Peak picking on the LP spectrum
Factoring for the LP roots
Fitting of mixture of Gaussians
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

38. Formant Analysis Track Draw: Formant Location Algorithm
2018/11/17
Formant Analysis
Track Draw:
A package for formant synthesis with options to sketch formant tracks on a spectrogram.
Formant Location Algorithm
MATLAB code by Michelle Jamrozik
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

39. Speech Waveform Coding
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Speech Waveform Coding
Time domain coding
PCM: Pulse Code Modulation
DPCM: Differential PCM
ADPCM: Adaptive Differential PCM (dspadpcm.mdl)
Frequency domain coding
Sub-band coding
Transform coding
Speech Coding in MATLAB
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

40. Conclusions Ideal tools for speech/audio signal processing:
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Conclusions
Ideal tools for speech/audio signal processing:
MATLAB
Simulink
Signal Processing Toolbox
DSP Blockset
Advantages:
Reliable functions: well-established and tested
Visible graphical algorithm design tools
High-level programming language yet C-compatible
Powerful visualization capabilities
Easy debugging
Integrated environment
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17

41. 2018/11/17
References
[1] “Discrete-Time Processing of Speech Signals”, by Deller, Proakis and Hansen, Prentice Hall, 1993
[2] “Fundamentals of Speech Recognition”, by Rabiner and Juang, Prentice Hall, 1993
[3] “Effects Explained”,
[4] “TrackDraw”,
[5] “Speech Coding in MATLAB”,
Specifically, this is the outline of the talk. Wel start from the basics, introduce the concepts of fuzzy sets and membership functions. By using fuzzy sets, we can formulate fuzzy if-then rules, which are commonly used in our daily expressions. We can use a collection of fuzzy rules to describe a system behavior; this forms the fuzzy inference system, or fuzzy controller if used in control systems. In particular, we can can apply neural networks?learning method in a fuzzy inference system. A fuzzy inference system with learning capability is called ANFIS, stands for adaptive neuro-fuzzy inference system. Actually, ANFIS is already available in the current version of FLT, but it has certain restrictions. We are going to remove some of these restrictions in the next version of FLT. Most of all, we are going to have an on-line ANFIS block for SIMULINK; this block has on-line learning capability and it ideal for on-line adaptive neuro-fuzzy control applications. We will use this block in our demos; one is inverse learning and the other is feedback linearization.
2018/11/17