In [1]:

```
%matplotlib inline
import matplotlib.pyplot as pl
import seaborn as sb
import numpy as np
from scipy.io import wavfile as wv
from scipy.signal import chirp
from matplotlib import cm
sb.set(style='ticks', font_scale=2)
blue, green, red, purple = sb.color_palette("deep",4)
```

Python has a number of powerful tools for reading, analyzing, manipulating, and generating signals. This can be very useful for acoustic analysis of speech, analysis of EEG recordings, generation of sounds for experiments, and various other things.

In discussing digital signal processing, we'll be using sinusoids a lot. We've seen these a little bit already, but we'll review the basics here.

Here's a very brief (and very incomplete) review of trigonometry. We can illustrate the relationships between sine and cosine by inscribing a right triangle inside a unit circle (i.e., a circle with radius one):

For the angle $\theta$ between the x-axis and the hypotenuse of the triangle, the sine of $\theta$ is the height of the triangle, and the cosine of $\theta$ is the base of the triangle. More generally, if the length of the hypotenuse (or, equivalently, the radius of the circle) is $r$, the height is $h$, and the width (base) is $w$, then:

\begin{align} \sin(\theta) &= \frac{opposite}{hypotenuse} = \frac{h}{r}\\ \cos(\theta) &= \frac{adjacent}{hypotenuse} = \frac{w}{r} \end{align}

If we imagine rotating the hypotenuse counter-clockwise, sweeping it over values of $\theta$ ranging from 0 to $2\pi$ (radians, i.e., $360^{\circ}$, or around the whole circle), we can plot `sin`

and `cos`

as *functions* of $\theta$:

In [2]:

```
# sampling frequency & sampling period
f_s = 1000
t_s = 1/f_s
# vector of theta values
th = np.linspace(start=0, stop=2*np.pi, num=f_s, endpoint=False)
s = np.sin(th)
c = np.cos(th)
# plot s and c
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot((th[0],th[-1]),(0,0),':',color=[.5,.5,.5,.85])
ax.set_xticks(np.arange(0,2*np.pi+.01,np.pi/2))
ax.set_xticklabels(['0',r'$\frac{\pi}{2}$',r'$\pi$',r'$\frac{3\pi}{2}$',r'$2\pi$'])
la, = ax.plot(th,s,lw=3, color=red)
lb, = ax.plot(th,c,lw=3, color=blue)
ax.legend((la,lb),(r'sin($\theta$)',r'cos($\theta$)'),loc='upper left', bbox_to_anchor=(1,.665))
ax.set(xlabel=r'$\theta$',ylabel=r'f($\theta$)')
sb.despine(ax=ax,trim=True)
```

Here is an animation of `sin`

and `cos`

and the radius of the unit circle.

The general formula for the cosine function of $\theta$ is $y = A \cos(\omega \theta + \phi)$. Here $A$ is the amplitude, $\omega$ is the frequency (in radians), and $\phi$ is the phase.

Note that $\displaystyle{\cos\left(\theta - \frac{\pi}{2}\right) = \sin\left(\theta\right)}$ (i.e., sin and cos are just phase-shifted copies of each other), so we can just pick one and use it (we will use `cos`

generally):

In [3]:

```
s = np.sin(th)
c = np.cos(th-np.pi/2)
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot((th[0],th[-1]),(0,0),':',color=[.5,.5,.5,.85])
ax.set_xticks(np.arange(0,2*np.pi+.01,np.pi/2))
ax.set_xticklabels(['0',r'$\frac{\pi}{2}$',r'$\pi$',r'$\frac{3\pi}{2}$',r'$2\pi$'])
la, = ax.plot(th,s,lw=3, color=red)
lb, = ax.plot(th,c,':',lw=5, color=blue)
ax.legend((la,lb),(r'sin($\theta$)',r'cos$\left(\theta-\pi/2\right)$'))
ax.set(xlabel=r'$\theta$',ylabel=r'f($\theta$)')
sb.despine(ax=ax,trim=True)
```

As we have seen in previous notebooks, changing the amplitude changes the maximum and minimum values the function reaches. Changing the frequency changes the rate at which the function repeats itself. And, as illustrated above, changing the phase shifts the function to the right or left.

In [4]:

```
# make four cos signals with different frequencies, phases
c_a = np.cos(th)
c_b = 4*np.cos(th + np.pi/5)
c_c = 1.5*np.cos(2*th)
c_d = 1.5*np.cos(2*th - np.pi/3)
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot((th[0],th[-1]),(0,0),':',color=[.5,.5,.5,.85])
ax.set_xticks(np.arange(0,2*np.pi+.01,np.pi/2))
ax.set_xticklabels(['0',r'$\frac{\pi}{2}$',r'$\pi$',r'$\frac{3\pi}{2}$',r'$2\pi$'])
la, = ax.plot(th,c_a,lw=3)
lb, = ax.plot(th,c_b,lw=3)
lc, = ax.plot(th,c_c,'--',lw=3)
ld, = ax.plot(th,c_d,'--',lw=3)
ax.legend((la,lb,lc,ld),(r'cos($\theta$)',r'4 cos($\theta - \pi/5$)',r'1.5 cos(2$\theta$)',r'1.5 cos(2$\theta-\pi$/3)'),
loc='upper left', bbox_to_anchor=(1,.78))
ax.set(xlabel=r'$\theta$',ylabel=r'f($\theta$)')
sb.despine(ax=ax,trim=True)
```

If we want to think in terms of time in seconds, which is often more intuitive, we can use a vector `t`

ranging from, e.g., 0 to 1, and then we would express the frequency in Hz. We need to multiply the `t`

vector by $2\pi$ (i.e., $\omega = 2\pi f_o$, where $f_o$ is the frequency in Hz), since the trigonometric functions assume the input is in radians:

In [5]:

```
# time vector
t = np.linspace(start=0, stop=1, num=f_s, endpoint=False)
# multiply frequency by 2*pi
s = 1.5*np.cos(2*np.pi*4*t)
c = 5*np.cos(2*np.pi*8*t)
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot((t[0],t[-1]),(0,0),':',color=[.5,.5,.5,.85])
la, = ax.plot(t,s,lw=3)
lb, = ax.plot(t,c,lw=3)
ax.legend((la,lb),('1.5 cos(2$\pi$4t)','5 cos(2$\pi$8t)'),loc='upper left', bbox_to_anchor=(1,.63))
ax.set(xlabel='t',ylabel=r'f(t)')
sb.despine(ax=ax,trim=True)
```

The signals we've generated thus far would all be pure tones if we saved them as wavefiles and listened to them. However, the frequency of all of them is below the lowest frequency that humans can hear. We can use what we have discussed so far to generate an audible pure tone:

In [6]:

```
# use same time vector to make 250 Hz tone
fs_10k = 10000
tt = np.linspace(start=0,stop=1,num=fs_10k,endpoint=False)
f_o = 250
pt = .975*np.cos(2*np.pi*f_o*tt) # amplitude < 1 to avoid clipping
wv.write(filename='pure_tone.wav', rate=fs_10k, data=pt) # save pt as a wavefile
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot(tt,pt); ax.set(ylabel='cos(2$\pi$250t)',xlabel='t')
sb.despine(ax=ax,trim=True)
```

In [7]:

```
# plot just a part of the higher frequency sinusoid
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot(tt[4750:5250],pt[4750:5250], lw=2); ax.set(ylabel='cos(2$\pi$250t)',xlabel='t')
sb.despine(ax=ax,trim=True)
```

All of the signal-processing data we'll be generating and/or analyzing will be periodically sampled. Sometimes we'll be analyzing sampled physical signals (e.g., digital representations of sounds). Other times we'll be creating sequences of sounds that are similarly structured. Periodic sampling (which we'll just call *sampling* from now on) is most easily understood via illustration.

First, let's define a couple terms: (sampling) frequency is the reciprocal of (sampling) period: $f_s = \displaystyle{\frac{1}{t_s}}$

So, let's start with a continuous sinusoidal physical signal: $x(t) = \cos(2\pi f_o t + \phi)$

If we sample this every $t_s$ seconds (i.e., with period $t_s$), we get $x[n] = \cos(2\pi f_o n t_s + \phi), n \in \{0, 1, 2, 3, ...\}$. So,

$\begin{align} x[0] &= \cos(2\pi f_o 0 t_s + \phi)\\ x[1] &= \cos(2\pi f_o 1 t_s + \phi)\\ x[2] &= \cos(2\pi f_o 2 t_s + \phi)\\ x[3] &= \cos(2\pi f_o 3 t_s + \phi)\\ &\vdots \end{align}$

We'll illustrate sampling with some figures. First create a time vector `t`

, a "continuous" sinusoid `y`

, and a sampled sinusoid `x`

:

In [8]:

```
# signal frequency
fo = 2
# "continuous" time
t = np.linspace(0,1,1000, endpoint=False)
# sampling frequency and period
f_s = 10
t_s = 1/f_s
# "continuous" signal
y = np.cos(2*np.pi*fo*t + np.pi/3)
# initialize sampled signal and time points
n,x = [],[]
# ni iterates through the list [0, 1, ..., fs]
for ni in range(f_s):
n.append(ni) # sample ni
x.append(np.cos(2*np.pi*fo*ni*t_s + np.pi/3)) # sampled cosine for sample ni
x = np.array(x)
n = np.array(n)
```

Plot the samples by themselves:

In [9]:

```
# plot just the samples
fig, ax = pl.subplots(1,1,figsize=(15,5))
ml, sl, bl = ax.stem(n/f_s,x,linefmt='r-',markerfmt='rs',basefmt='k:')
ax.set(ylim=(-1.1,1.1),xlim=(-.05,1.05),xlabel='t',ylabel='x[n]')
sb.despine(ax=ax, trim=True)
```

And here are the samples along with the "continuous" signal:

In [10]:

```
# plot the samples and the "continuous" signal
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot(t,y,lw=3, color=blue)
ax.stem(n/f_s,x,linefmt='r-',markerfmt='rs',basefmt='k:')
ax.set(ylim=(-1.1,1.1),xlim=(-.05,1.05),xlabel='t',ylabel='y(t), x[n]')
sb.despine(ax=ax, trim=True)
```

A crucial fact about sampling is that it produces some ambiguity. Because `sin()`

and `cos()`

are periodic, with period $2\pi$, for any integer $m$:

$\cos(2\pi f_o n t_s) = \cos(2\pi f_o n t_s + 2\pi m) = \cos\left(2\pi \left(f_o + \displaystyle{\frac{m}{nt_s}} \right) nt_s\right)$

If we let $m = kn$, this equals $\cos\left(2\pi \left(f_o + \displaystyle{\frac{k}{t_s}} \right)nt_s\right) = \cos\left(2\pi \left(f_o + kf_s \right) nt_s \right)$

The point is that the original frequency $f_o$ *plus any integer multiple of the sampling frequency* will produce exactly the same samples as our original signal.

Here is an illustration of this fact, showing that the samples illustrated above could have come from either the original signal or a signal with frequency $f_o + kf_s$ (we can play around with $k$ to convince ourselves that it works for any integer multiple of the sampling frequency):

In [11]:

```
# create "aliased" signal, with fa = fo + k*fs
fa = fo + 1*f_s
y2 = np.cos(2*np.pi*fa*t + np.pi/3)
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot([-1.1,1.1],[0,0],'k:')
ax.set(ylim=(-1.1,1.1),xlim=(-.05,1.05),xlabel='t',ylabel='y(t), x[n]')
ax.plot(t,y,lw=3)
ax.plot(t,y2,lw=3)
ax.stem(n/f_s,x,linefmt='r-',markerfmt='rs',basefmt='w:')
sb.despine(ax=ax, trim=True)
```

So, different frequencies in a continuous signal can produce identical samples in a discrete-time counterpart to the physical signal. When a higher-frequency signal looks like a lower-frequency signal in a sampled sequence of values, this is called *aliasing*. We will return to this issue after we've discussed spectral representations of signals.

For now, I will just note that $\displaystyle{\frac{f_s}{2}}$ is known as the Nyquist frequency. This is a special value that you should always keep in the back of your mind. When you sample a signal with a sampling frequency of $f_s$, the highest frequency you can accurately represent in your sampled signal is $\displaystyle{\frac{f_s}{2}}$. Any frequency in the signal that is above the Nyquist frequency will be aliased onto a frequency below the Nyquist frequency.

This is illustrated in the figure above for $f_o = 2$, $f_s = 10$, and $f_a = f_o + f_s$. The frequency $f_a$ shows up as $f_o$ in the sampled signal, due to aliasing.

The Fourier transform is a tool for decomposing a signal into its component parts. More specifically, it takes a signal in the *time domain* and represents it in the *frequency domain*. Thus far, we've looked at signals only in the time domain (e.g., when we made sinusoids using a time vector `t`

above).

(Here is a nice new video explaining the Fourier transform visually.)

The *discrete* Fourier transform (DFT) is a special case of the Fourier transform that is used with discrete-time signals (i.e., sampled time-domain signals). The DFT is extremely common and extremely useful in digital signal processing.

The definition of the DFT is given below. It's kind of complicated, so we'll unpack it in some detail to try build our intuitions about how the time domain and the frequency domain are related. (The following description and illustration of the DFT is based on Richard Lyons' presentation of the DFT in Understanding Digitial Signal Processing, which is an excellent introductory DSP book.)

Here is the definition of the DFT:

$\begin{equation}X[m] = \displaystyle{\sum_{n=0}^{N-1} x[n] \exp\left(\frac{-i 2 \pi n m}{N}\right)}\end{equation}$

Here are the individual parts of the equation with some explanation:

$x[n]$ is just a sequence of samples from whatever signal we're interested in analyzing (e.g., a vector of samples from a sound file, from neural recordings, etc...)

$\exp(\theta)$ is the exponential function, i.e., $e^\theta$, where $e \approx 2.718$ (more here, if you're interested).

You know what $\pi$ is, I assume.

$n$ is the index of the *time domain* sequence $x[n]$. That is, $n$ tells us where a given sample is with respect to time.

$m$ is the index of the *frequency domain* sequence $X[m]$. That is, $m$ tells us where a given sample is with respect to frequency.

$N$ is the number of samples we're dealing with in any particular DFT. Note that the number of samples in the time domain signal is equal to the number of samples in the frequency domain transform.

$i = \sqrt{-1}$, or, equivalently, $i^2 = -1$, and its presence indicates that we're dealing with complex numbers. Sometimes $j$ is used instead of $i$.

When $e$ shows up with a power of $i$, it is called a *complex exponential* function, and it is defined, via Euler's equation, as:

$\exp(i \theta) = \cos(\theta) + i\sin(\theta)$

If the $i$ is negative, it's the *complex conjugate* of the form with positive $i$:

$\exp(-i \theta) = \cos(\theta) - i\sin(\theta)$

Euler's equation implies that the DFT equation can also be written as:

$\begin{equation}X[m] = \displaystyle{\sum_{n=0}^{N-1} x[n] \left[ \cos\left(\frac{2 \pi n m}{N}\right) - i\sin\left(\frac{2 \pi n m}{N}\right)\right]}\end{equation}$

As we will illustrate below, this way of writing the DFT shows that it is an expression of correlations (cross-products) between a time domain signal $x[n]$ and sinusoids of various frequencies.

A complex number has the form $x = a + ib$, where, again, $i^2 = -1$. We say that $a$ is the real part and $b$ is the imaginary part.

To add two complex numbers, just add the real and imaginary parts: $(a + ib) + (c + id) = (a+c) + i(b+d)$

To multiply two complex numbers, act like you're multiplying polynomials: $(a + ib)(c + id) = ac + iad + ibc - bd = (ac-bd) + i(ad + bc)$.

Multiplying complex exponentials (as in the DFT equation above) is easier: $\big(\cos(\theta) + i\sin(\theta)\big)\big(\cos(\gamma) + i\sin(\gamma)\big) = e^{i\theta}e^{i\gamma} = e^{i(\theta + \gamma)}$

The magnitude of a complex number is $|x| = \sqrt{a^2 + b^2}$. Note that the square root of the product of a complex number and it's complex conjugate produces the magnitude, since $(a + ib)(a - ib) = a^2 - iab + iab - i^2 b^2 = a^2 + b^2$.

The phase angle of a complex number is $\displaystyle{x_\phi = \tan^{-1}\left(\frac{b}{a}\right)}$

It's useful to plot complex numbers in a 2D space where the x axis is the real part ($a$) and the y axis is the imaginary part ($b$):

In [12]:

```
fig, ax = pl.subplots(1,1,figsize=(10,5))
x_c = 4 + 2j # create a complex number
# plot x and y axes as dotted lines
ax.plot([0,0],[-.2,4],':',color=[.5,.5,.5,.75])
ax.plot([-.2,4],[0,0],':',color=[.5,.5,.5,.75])
# plot real and imaginary parts, plot magnitude and angle
ax.plot(x_c.real,x_c.imag,'o',markersize=10)
ax.plot([0,x_c.real],[0,x_c.imag],'-',lw=4,color=[.8,.1,.8,.9])
ax.plot([0,x_c.real],[0,0],'-',lw=4,color=[.8,.05,0,.9])
ax.plot([0,0],[0,x_c.imag],'-',lw=4,color=[0,.05,.8,.9])
ax.plot([0,x_c.real],[x_c.imag,x_c.imag],':',color=[.5,.5,.5,.75])
ax.plot([x_c.real,x_c.real],[0,x_c.imag],':',color=[.5,.5,.5,.75])
ax.text(.75,.15,r'$\theta$',fontsize=24)
# fiddle with the axes to make labels and set limits and such
ax.set(ylabel='Imag(x_c)',xlabel='Real(x_c)',xlim=(-.1,5.5),ylim=(-.1,2.5))
# add annotations
ap = dict(facecolor='black',shrink=.1)
ax.annotate('x_c = 4 + 2j',xy=(x_c.real,x_c.imag),xytext=(4.5,1.5),arrowprops=ap,fontsize=18)
ax.annotate('magnitude',xy=(x_c.real,x_c.imag),xytext=(x_c.real-2.5,x_c.imag-.7),arrowprops=ap,fontsize=18)
ax.annotate('magnitude',xy=(0,0),xytext=(x_c.real-2.5,x_c.imag-.7),arrowprops=ap,fontsize=18)
ax.annotate('real part',xy=(2.4,0.03),xytext=(2.2,.5),arrowprops=ap,fontsize=18)
ax.annotate('imaginary part',xy=(0.03,1.1),xytext=(.75,1.75),arrowprops=ap,fontsize=18)
ax.annotate('angle',xy=(.85,.25),xytext=(1.5,.6),arrowprops=ap,fontsize=18)
sb.despine(ax=ax,trim=True)
```

It is helpful to see the full, expanded DFT equation for the first few terms of a simple example. So, suppose we have the following signal $x[n]$ sampled at $f_s = 8000$ Hz:

$x(t) = \sin(2\pi 1000 t) + 0.5\sin(2\pi 3000 t + 3\pi/4)$

Here's what the first two milliseconds of this signal look like, in (fake) continuous form and in the sampled form we'll be analyzing:

In [13]:

```
# a "continuous" looking version of x
fs = 50000 # set sampling frequency
sc = .002 # two milliseconds as a fraction of a second
nsmp = int(fs*sc) # the number of samples in two milliseconds
tc = np.linspace(0,sc,nsmp,endpoint=False)
# the signal of interest
x = np.sin(2*np.pi*1000*tc) + .5*np.sin(2*np.pi*3000*tc + 3*np.pi/4)
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot(tc,x,lw=3); sb.despine(ax=ax,trim=True); ax.set(xlabel='t',ylabel='x(t)', xticks=np.linspace(0,.002,5));
```

In [14]:

```
# the discrete-time signal we'll actually analyze
# set sampling frequency
fs = 8000
# number of samples in two milliseconds
nsmp = int(fs*sc)
# discrete time vector
td = np.linspace(0,sc,nsmp,endpoint=False)
# the sampled signal of interest
x = np.sin(2*np.pi*1000*td) + .5*np.sin(2*np.pi*3000*td + 3*np.pi/4)
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.stem(td,x); ax.set(xlabel='t',ylabel='x[t]', xticks=np.linspace(0,.002,5)); sb.despine(ax=ax,trim=True);
```

Now, suppose we want to calculate the 8-point DFT for the first 8 samples of $x[n]$. We're going to apply the DFT equation above to the first 8 samples of $x[n]$, and we'll get 8 samples in the frequency domain. That is, we'll get an 8-point sequence that provides us with information about the frequency and phase of the components of $x[n]$.

If we expand the DFT equation above for the first element of $X[m]$, we get:

$\begin{align} X[0] &= \displaystyle{\sum_{n=0}^{7} x[n] \left[ \cos(2 \pi \cdot n \cdot 0/N) - i\sin(2 \pi \cdot n \cdot 0/N)\right]}\\ &= x[0]\cos(2\pi \cdot 0 \cdot 0 / 8) - ix[0]\sin(2\pi \cdot 0 \cdot 0 /8 )\\ &\quad + x[1]\cos(2\pi \cdot 1 \cdot 0 / 8) - ix[1]\sin(2\pi \cdot 1 \cdot 0 /8 )\\ &\quad + x[2]\cos(2\pi \cdot 2 \cdot 0 / 8) - ix[2]\sin(2\pi \cdot 2 \cdot 0 /8 )\\ &\quad + x[3]\cos(2\pi \cdot 3 \cdot 0 / 8) - ix[3]\sin(2\pi \cdot 3 \cdot 0 /8 )\\ &\quad + x[4]\cos(2\pi \cdot 4 \cdot 0 / 8) - ix[4]\sin(2\pi \cdot 4 \cdot 0 /8 )\\ &\quad + x[5]\cos(2\pi \cdot 5 \cdot 0 / 8) - ix[5]\sin(2\pi \cdot 5 \cdot 0 /8 )\\ &\quad + x[6]\cos(2\pi \cdot 6 \cdot 0 / 8) - ix[6]\sin(2\pi \cdot 6 \cdot 0 /8 )\\ &\quad + x[7]\cos(2\pi \cdot 7 \cdot 0 / 8) - ix[7]\sin(2\pi \cdot 7 \cdot 0 /8 )\\ \end{align}$

Since $\cos(0) = 1$ and $\sin(0) = 0$, this first term - $X[0]$ - is just the sum of the first 8 samples of $x[n]$.

The second element of the DFT - $X[1]$ - is given by:

$\begin{align} X[1] &= \displaystyle{\sum_{n=0}^{7} x[n] \left[ \cos(2 \pi \cdot n \cdot 1/N) - i\sin(2 \pi \cdot n \cdot 1/N)\right]}\\ &= x[0]\cos(2\pi \cdot 0 \cdot 1 / 8) - ix[0]\sin(2\pi \cdot 0 \cdot 1 /8 )\\ &\quad + x[1]\cos(2\pi \cdot 1 \cdot 1 / 8) - ix[1]\sin(2\pi \cdot 1 \cdot 1 /8 )\\ &\quad + x[2]\cos(2\pi \cdot 2 \cdot 1 / 8) - ix[2]\sin(2\pi \cdot 2 \cdot 1 /8 )\\ &\quad + x[3]\cos(2\pi \cdot 3 \cdot 1 / 8) - ix[3]\sin(2\pi \cdot 3 \cdot 1 /8 )\\ &\quad + x[4]\cos(2\pi \cdot 4 \cdot 1 / 8) - ix[4]\sin(2\pi \cdot 4 \cdot 1 /8 )\\ &\quad + x[5]\cos(2\pi \cdot 5 \cdot 1 / 8) - ix[5]\sin(2\pi \cdot 5 \cdot 1 /8 )\\ &\quad + x[6]\cos(2\pi \cdot 6 \cdot 1 / 8) - ix[6]\sin(2\pi \cdot 6 \cdot 1 /8 )\\ &\quad + x[7]\cos(2\pi \cdot 7 \cdot 1 / 8) - ix[7]\sin(2\pi \cdot 7 \cdot 1 /8 )\\ \end{align}$

And the third - $X[2]$ - is given by:

$\begin{align} X[2] &= \displaystyle{\sum_{n=0}^{7} x[n] \left[ \cos(2 \pi \cdot n \cdot 2/N) - i\sin(2 \pi \cdot n \cdot 2/N)\right]}\\ &= x[0]\cos(2\pi \cdot 0 \cdot 2 / 8) - ix[0]\sin(2\pi \cdot 0 \cdot 2 /8 )\\ &\quad + x[1]\cos(2\pi \cdot 1 \cdot 2 / 8) - ix[1]\sin(2\pi \cdot 1 \cdot 2 /8 )\\ &\quad + x[2]\cos(2\pi \cdot 2 \cdot 2 / 8) - ix[2]\sin(2\pi \cdot 2 \cdot 2 /8 )\\ &\quad + x[3]\cos(2\pi \cdot 3 \cdot 2 / 8) - ix[3]\sin(2\pi \cdot 3 \cdot 2 /8 )\\ &\quad + x[4]\cos(2\pi \cdot 4 \cdot 2 / 8) - ix[4]\sin(2\pi \cdot 4 \cdot 2 /8 )\\ &\quad + x[5]\cos(2\pi \cdot 5 \cdot 2 / 8) - ix[5]\sin(2\pi \cdot 5 \cdot 2 /8 )\\ &\quad + x[6]\cos(2\pi \cdot 6 \cdot 2 / 8) - ix[6]\sin(2\pi \cdot 6 \cdot 2 /8 )\\ &\quad + x[7]\cos(2\pi \cdot 7 \cdot 2 / 8) - ix[7]\sin(2\pi \cdot 7 \cdot 2 /8 )\\ \end{align}$

And so on. For element $m$ of the DFT, we are calculating the correlation between our input signal $x[n]$ and *analysis frequencies* corresponding to the $m$ indices. The actual frequency values indexed by $m$ are given by $\displaystyle{m\frac{f_s}{N}}$, so, e.g., when $m = 1$, we're gathering information about the analysis frequency $f_a = \displaystyle{1\cdot\frac{8000}{8}} = 1000$ Hz. When $m = 2$, we're dealing with $\displaystyle{2\cdot\frac{8000}{8}} = 2000$ Hz, and so on.

Some figures will help to illustrate what's going on here. First, we'll set up "continuous" and discrete time vectors and signals:

In [15]:

```
# one ms as a fraction of a second (rather than two)
sc = .001
# "continuous" time
fs = 100000
# number of samples in one ms
nsmp = int(fs*sc)
# "continuous" time vector
tc = np.linspace(0,sc,nsmp,endpoint=False)
# signal
xc = np.sin(2*np.pi*1000*tc) + .5*np.sin(2*np.pi*3000*tc + 3*np.pi/4)
# discrete time
fs = 8000
# number of samples in one ms
nsmp = int(fs*sc)
# discrete time vector
td = np.linspace(0,sc,nsmp,endpoint=False)
# signal
xd = np.sin(2*np.pi*1000*td) + .5*np.sin(2*np.pi*3000*td + 3*np.pi/4)
```

When $m = 1$, our analysis frequency is 1000 Hz, so when we calculate the DFT equation, we calculate the correlation between the 8 samples of our signal and analysis sinusoids (cosines and sines) with frequency 1000 Hz (i.e., one complete cycle in the 8 samples we're looking at):

In [16]:

```
# m = 1, f_a = 1000 Hz
# number of points in time and frequency domains
N = 8
# analytic signals for m = 1 -> 1000 Hz
cosine = np.cos(2*np.pi*1000*tc)
sine = np.sin(2*np.pi*1000*tc)
# plot analytic signals and input signal x
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot(tc,cosine,'r-.',lw=3)
ax.plot(tc,sine,'b--',lw=3)
ax.plot(tc,xc,'g-',lw=3)
ax.plot(td,xd,'gs',ms=12)
ax.text(.00105,0,'m = 1', fontsize=20)
ax.set(xlabel='t',ylabel='x[t]', xticks=np.linspace(0,.001,5)); sb.despine(ax=ax,trim=True);
```

When $m = 2$, our analysis frequency is 2000 Hz, so we're calculating the correlation with analysis sinusoids with frequency 2000 Hz (i.e., two complete cycles in our 8 samples):

In [17]:

```
# analytic signals for m = 2 -> 2000 Hz
cosine = np.cos(2*np.pi*2000*tc)
sine = np.sin(2*np.pi*2000*tc)
# plot analytic signals and input signal x
fig, ax = pl.subplots(1,1,figsize=(15,5))
ax.plot(tc,cosine,'r-.',lw=3)
ax.plot(tc,sine,'b--',lw=3)
ax.plot(tc,xc,'g-',lw=3)
ax.plot(td,xd,'gs',ms=12)
ax.text(.00105,0,'m = 2', fontsize=20)
ax.set(xlabel='t',ylabel='x[t]', xticks=np.linspace(0,.001,5)); sb.despine(ax=ax,trim=True);
```