The purpose of this lab is to get you familiarized with using the Fast Fourier Transform (FFT) to study the frequency content of discrete-time signals. You will firstly inspect and describe the frequency contents of some generated signals and provided audio signals. Then, you will examine how several transformations on these signals affect the frequency content of them.
Task 1: Using the FFT function
from scipy.fftpack import *
In this task you will learn how to use the fft function provided by the SciPy package. Firstly, to use fft, you need to import the scipy.fftpack package as follows.
Put it after your other import statements. In this lab you will also need numpy, pyplot, and simpleaudio. Refer to the previous labs on how to import or use them.
Next, refer to the reference document (here) of the fft function to have a general overview of the parameters and usages of the function. The fft function computes the Discrete Fourier Transform (DFT) of an array. The result of fft is an array of complex numbers so you will need to look at the magnitude and phase of them separately. The functions np.abs and np.angle are useful for obtaining the magnitude and phase of a complex number or a complex-valued array. Also, since the FFT only has values at discrete frequencies, it may be useful to do the plots with plt.stem to reinforce the idea, but continuous frequency plots (i.e. using plt.plot) are often used since they are closer to the DTFT that you are ultimately interested in.
The FFT outputs an array that the indices correspond to the range 0 β€ π < 2π (or 0 β€ π < 1). You are probably more familiar with seeing the spectrum plotted over the range βπ β€ π < π (or β0.5 β€ π < 0.5). The fftshift function can be used for this purpose.
Assignment
Plot the magnitude of the FFT of the following signal before and after the fftshift: π₯[π] = 1 + πππ (2Οππ) ; 0 β€ π < 128
for the cases where π = 0.25 and π = 0.5.
In your notebook, include the 3-part plot (using plt.stem) for each signal: unshifted DFT, shifted DFT and the shifted DFT with Hertz frequency assuming a sampling rate of 10 kHz. Discuss why the frequency peak locations make sense.
Task 2: Frequency Shifting
Recall that multiplying by a complex exponential in time (or a cosine, which is comprised of a pair of complex
exponentials) results in a frequency shift. For each of the following sequences, let π = 0.15 and 0 β€ π < 256. 1
Developed/revised by Yibo Cao, Kamil Jiwa, and Tai Chen 2019
To evaluate sinc, use the np.sinc function provided by NumPy. Plot the magnitude and phase plots (using plt.plot), where the magnitude and phase plots are over the range β0.5 β€ π < 0.5 (normalized frequency), i.e. use fftshift.
(a) π₯ [π]=sinc(π β (πβ32)) 11
(b) π₯ [π]=sinc(π β (πβ32))β (β1)π 21
(c) π₯ [π]=sinc(π β (πβ32))β πππ (2Οππ)whereπ =0.2 3122
(d) π₯ [π]=sinc(π β (πβ32))β πππ (2Οππ)whereπ =0.4 4133
Display the plots for (a) β (d). Treating the plots as frequency response of filters, state what type of filter each of them corresponds to (low pass, high pass, etc.). For (d), explain why (d) does not have a flat frequency response in the passband.
Task 3: Starting from Continuous Time Signals
Download two sounds files from the class website, picking one that you think will have more high-frequency content and one that will have more low-frequency content. Load each sound using the function provided in Appendix A. Record the sampling rate and length of the samples (in seconds). Play the sound using the following statement introduced in Lab 1:
sa.play_buffer((samples * 32767).astype(‘int16’), 1, 2, sr).wait_done()
where samples is the array of the audio samples and sr is the sampling rate.
Plot the time-domain waveform and the FFT of each sound. For the frequency plot, use your understanding of the relation between discrete and continuous time and knowledge of the sampling rate to scale the frequency axis to match the continuous time range in Hertz. Comment on whether the frequency content matches your expectation. Then, perform the following modifications to the signals.
- (a) Β Modify your signals by multiplying the time-domain signal by either (β1)π as in Task 2 (b), or a cosine as in Task 2 (c). Play the sounds and plot the frequency content of the new sounds. What is the effect of the frequency shift on how they sound compared to the original?
- (b) Β Modify your signals by time-scaling: π¦[π] = π₯[2π], which results in frequency scaling. Play the sounds and plot the frequency content. How does frequency scaling compare to frequency shifting?
- (c) Β One way to implement an ideal filter is to zero out frequency terms. Implement a high-pass filter with a cut-off of π = 0.25 by zeroing out the low frequency terms in the FFT of the sounds you chose. Then
take the inverse FFT of the result using ifft and ifftshift to get the modified signal. Play the sounds and plot the frequency content of the new sounds. (You may need to scale the filtered sound if it is hard to hear, since youβve eliminated a lot of the energy in it.) Discuss the impact of this operation on the sounds.
In your notebook, include the frequency plots for the original and modified versions of each of your sounds. Provide details of the modifications that you did in each part and explain the impact of these operations on what you hear.
EE 341 Lab 3
π
Lab Submission Requirements
Submit your completed Jupyter Notebook in the ipynb format to Canvas. The notebook file can be obtained by clicking the menu: File β Download as β Notebook or directly accessed from the ee341lab folder in your home directory. Make sure to include the code of all the 3 tasks in code cells and include the required discussions of each task in text cells.
Appendix A: Loading WAV Files
To load sound data from WAV files into your Python program, firstly copy the files you want to load into the same directory where your Jupyter Notebook is stored (that is the ee341lab folder in your home directory if you followed our convention in Lab 1). Then, add the following import statement after your other imports:
import scipy.io.wavfile as wav
Ideally, you should be able to directly use wav.read from this package to load files. However, WAV files can have different quantization formats and number of channels that will cause troubles later on. Therefore, we provide you the following helper function to help you deal with different formats.
EE 341 Lab 3
# Load a WAV file.
# Return the sampling rate and the sample array. def wav_load(file_name):
# Load the raw data.
sr, data = wav.read(file_name) # Only use the first channel. if data.ndim > 1:
data = data[:, 0]
# Convert to float32 quantization. kind = data.dtype.kind
bits = data.dtype.itemsize * 8 data = data.astype(‘float32’)
if kind == ‘i’ or kind == ‘u’:
data = data / 2 ** (bits – 1) if kind == ‘u’:
data = data – 1 return sr, data
Example usage:
sr, samples = wav_load(“do.wav”)
This will load the file do.wav, putting the sampling rate into the variable sr and putting the sample array into
the variable samples.
