mplement a basic digital music synthesizer and use it to play a traditional song in a three-voice arrangement. Specify a sample rate of 2 kHz. Save the song as a MATLAB® timetable.
fs = 2e3; t = 0:1/fs:0.3-1/fs; l = [0 130.81 146.83 164.81 174.61 196.00 220 246.94]; m = [0 261.63 293.66 329.63 349.23 392.00 440 493.88]; h = [0 523.25 587.33 659.25 698.46 783.99 880 987.77]; note = @(f,g) [1 1 1]*sin(2*pi*[l(g) m(g) h(f)]'.*t); mel = [3 2 1 2 3 3 3 0 2 2 2 0 3 5 5 0 3 2 1 2 3 3 3 3 2 2 3 2 1]+1; acc = [3 0 5 0 3 0 3 3 2 0 2 2 3 0 5 5 3 0 5 0 3 3 3 0 2 2 3 0 1]+1; song = []; for kj = 1:length(mel) song = [song note(mel(kj),acc(kj)) zeros(1,0.01*fs)]; end song = song'/(max(abs(song))+0.1); % To hear, type sound(song,fs) tune = timetable(seconds((0:length(song)-1)'/fs),song);
Open Signal Analyzer and drag the timetable from the Workspace browser to the Signal table. Click Display Grid ? to create a two-by-two grid of displays. Select the top two displays and the lower left display and click the Spectrum button to add a spectrum view. Select the lower right display, click Time-Frequency to add a spectrogram view, and click Time to remove the time view. Drag the song to all four displays. Select the lower right display, and in the Spectrogram tab, specify a time resolution of 0.31 second (310 ms) and 0% overlap between adjoining segments. Set the Power Limits to −50 dB and −10 dB.
On the Analyzer tab, click Duplicate three times to create three copies of the song. Rename the copies as high
, medium
, and low
by double-clicking the Name column in the Signal table. Move the copies to the top two and lower left displays.
Preprocess the duplicate signals using filters.
Select the high
signal by clicking its name in the Signal table. On the Analyzer tab, click Highpass. On the Highpass tab that appears, enter a passband frequency of 450 Hz and increase the steepness to 0.95. Click Highpass.
Select the medium
signal by clicking its name in the Signal table. On the Analyzer tab, click Preprocessing ? and select Bandpass. On the Bandpass tab that appears, enter 230 Hz and 450 Hz as the lower and upper passband frequencies, respectively. Increase the steepness to 0.95. Click Bandpass.
Select the low
signal by clicking its name in the Signal table. On the Analyzer tab, click Lowpass. On the Lowpass tab that appears, enter a passband frequency of 230 Hz and increase the steepness to 0.95. Click Lowpass.
On each of the three displays containing filtered signals:
Remove the original signal by clearing the check box next to its name.
On the Display tab, click Time-Frequency to add a spectrogram view and click Time to remove the time view.
On the Spectrogram tab, specify a time resolution of 0.31 second and 0% overlap between adjoining segments. Set the Power Limits to −50 dB and −10 dB.
Select the three filtered signals by clicking their Name column in the Signal table. On the Analyzer tab, click Export and save the signals to a MAT-file called music.mat
. In MATLAB, load the file to the workspace. Plot the spectra of the three signals.
load music pspectrum(low) hold on pspectrum(medium) pspectrum(high) hold off
% To hear the different voices, type % sound(low.low,fs), pause(5), sound(medium.medium,fs), pause(5), sound(high.high,fs)
Matlabsolutions.com provides guaranteed satisfaction with a
commitment to complete the work within time. Combined with our meticulous work ethics and extensive domain
experience, We are the ideal partner for all your homework/assignment needs. We pledge to provide 24*7 support
to dissolve all your academic doubts. We are composed of 300+ esteemed Matlab and other experts who have been
empanelled after extensive research and quality check.
Matlabsolutions.com provides undivided attention to each Matlab
assignment order with a methodical approach to solution. Our network span is not restricted to US, UK and Australia rather extends to countries like Singapore, Canada and UAE. Our Matlab assignment help services
include Image Processing Assignments, Electrical Engineering Assignments, Matlab homework help, Matlab Research Paper help, Matlab Simulink help. Get your work
done at the best price in industry.
Desktop Basics - MATLAB & Simulink
Array Indexing - MATLAB & Simulink
Workspace Variables - MATLAB & Simulink
Text and Characters - MATLAB & Simulink
Calling Functions - MATLAB & Simulink
2-D and 3-D Plots - MATLAB & Simulink
Programming and Scripts - MATLAB & Simulink
Help and Documentation - MATLAB & Simulink
Creating, Concatenating, and Expanding Matrices - MATLAB & Simulink
Removing Rows or Columns from a Matrix
Reshaping and Rearranging Arrays
Add Title and Axis Labels to Chart
Change Color Scheme Using a Colormap
How Surface Plot Data Relates to a Colormap
How Image Data Relates to a Colormap
Time-Domain Response Data and Plots
Time-Domain Responses of Discrete-Time Model
Time-Domain Responses of MIMO Model
Time-Domain Responses of Multiple Models
Introduction: PID Controller Design
Introduction: Root Locus Controller Design
Introduction: Frequency Domain Methods for Controller Design
DC Motor Speed: PID Controller Design
DC Motor Position: PID Controller Design
Cruise Control: PID Controller Design
Suspension: Root Locus Controller Design
Aircraft Pitch: Root Locus Controller Design
Inverted Pendulum: Root Locus Controller Design
Get Started with Deep Network Designer
Create Simple Image Classification Network Using Deep Network Designer
Build Networks with Deep Network Designer
Classify Image Using GoogLeNet
Classify Webcam Images Using Deep Learning
Transfer Learning with Deep Network Designer
Train Deep Learning Network to Classify New Images
Deep Learning Processor Customization and IP Generation
Prototype Deep Learning Networks on FPGA
Deep Learning Processor Architecture
Deep Learning INT8 Quantization
Quantization of Deep Neural Networks
Custom Processor Configuration Workflow
Estimate Performance of Deep Learning Network by Using Custom Processor Configuration
Preprocess Images for Deep Learning
Preprocess Volumes for Deep Learning
Transfer Learning Using AlexNet
Time Series Forecasting Using Deep Learning
Create Simple Sequence Classification Network Using Deep Network Designer
Classify Image Using Pretrained Network
Train Classification Models in Classification Learner App
Train Regression Models in Regression Learner App
Explore the Random Number Generation UI
Logistic regression create generalized linear regression model - MATLAB fitglm 2
Support Vector Machines for Binary Classification
Support Vector Machines for Binary Classification 2
Support Vector Machines for Binary Classification 3
Support Vector Machines for Binary Classification 4
Support Vector Machines for Binary Classification 5
Assess Neural Network Classifier Performance
Discriminant Analysis Classification
Train Generalized Additive Model for Binary Classification
Train Generalized Additive Model for Binary Classification 2
Classification Using Nearest Neighbors
Classification Using Nearest Neighbors 2
Classification Using Nearest Neighbors 3
Classification Using Nearest Neighbors 4
Classification Using Nearest Neighbors 5
Gaussian Process Regression Models
Gaussian Process Regression Models 2
Understanding Support Vector Machine Regression
Extract Voices from Music Signal
Align Signals with Different Start Times
Find a Signal in a Measurement
Extract Features of a Clock Signal
Filtering Data With Signal Processing Toolbox Software
Find Periodicity Using Frequency Analysis
Find and Track Ridges Using Reassigned Spectrogram
Classify ECG Signals Using Long Short-Term Memory Networks
Waveform Segmentation Using Deep Learning
Label Signal Attributes, Regions of Interest, and Points
Introduction to Streaming Signal Processing in MATLAB
Filter Frames of a Noisy Sine Wave Signal in MATLAB
Filter Frames of a Noisy Sine Wave Signal in Simulink
Lowpass Filter Design in MATLAB
Tunable Lowpass Filtering of Noisy Input in Simulink
Signal Processing Acceleration Through Code Generation
Signal Visualization and Measurements in MATLAB
Estimate the Power Spectrum in MATLAB
Design of Decimators and Interpolators
Multirate Filtering in MATLAB and Simulink