## Time, Frequency, and the DFT

## Sound Is Frequencies

Most sounds have high periodicity

Fourier’s Theorem (FOO-ree-YAY or thereabouts) says that an infinitely repeating sound can be represented as a sum of sinusoids

The ear hears/decomposes a sum of sinusoids

Yet PCM is a sequence of samples over time: frequency is not represented

- The Nyquist Limit is hard to think of as a signal change over time thing

## Time and Frequency

We have: a continuous waveform, a function

*f*(*t*) representing sound pressureWe want: a continuous

*spectrum*, showing the amplitude and phase of sine waves at every frequency*f̂*(*ω*)Wait, amplitude and phase from a single function? Yes, representing a frequency as a complex number with the usual geometric interpretation

\[ f = a + b i, |f| = \sqrt{a^2 - b^2}, \theta(f) = tan^{-1}(a, b) \]

Note: you will see both

*i*and*j*for \(\sqrt{-1}\) in different contextsNote: we freely mix between

*angular frequency*\(\omega\) and “normal” frequency*f*(dammit) using\[ \omega = 2 \pi f \]

because once around the circle is one cycle

## The Euler Formula

Euler’s Formula says complex exponential is a sinusoid:

\[ e^{i (\omega t + \theta)} = cos(\omega t + \theta) + i~sin(\omega t + \theta) \]

\[ = e^{i \omega t} e^{i \theta} \]

Starting point for “phasor analysis”

Now our sum of sinusoids can be represented as a sum of exponentials, making things easier (?)

## The Time Domain and the Frequency Domain

Reminder: Fourier claims that every \(f(t)\) can be represented as some \(\hat{f}(\omega)\) (more or less)

We think of the first kind of thing as “in the frequency domain”, the second as “in the time domain”

Converting from a single frequency to its time domain representation is “easy”:

\[ f(t) = e^{-i \omega t} \]

Even for a single sinusoid, converting the other way isn’t immediately obvious

## Continuous Fourier Decomposition: The Fourier Transform

Let’s just get the Fourier Transform out there:

\[ \hat{f}(\omega) = \int_{-\infty}^{\infty} f(t) e^{-i \omega t} dt \]

The minus sign in the exponent is fiddly

The infinite integral is alarming

Still, we now can math up what we wanted

What about going the other way? Turns out that

\[ f(t) = \frac{1}{2 \pi} \int_{-\infty}^{\infty} \hat{f}(\omega) e^{i \omega t} d\omega \]

So the transform is

*almost*self-inverse

## The Discrete-Time Fourier Transform

Let’s “get rid of” the infinities by just taking limits

\[ \hat{f}(\omega) = \lim_{W \rightarrow \infty} \int_{-W}^{W} f(t) e^{-i \omega t} dt \]

A very long signal relative to its period should be well-approximated

Now a fiddly argument ensues. If the signal is periodic, we should be able to replace the limits by the period: after all, it doesn’t change anything

\[ \hat{f}(\omega) = \int_{0}^{P} f(t) e^{-i \omega t} dt \]

Let’s turn the integral into a sum by treating \(f(t)\) as a series of

*impulses*: nonzero only at discrete timesteps\[ X[k] = \sum_{n=0}^{N-1} x[n] e^{-i k n / N} \]

The frequency-domain value at

*unit frequency k*, namely \(X[k]\) has been written as a weighted sum of the discrete time-domain values \(x[i]\) over the period \(N\)The inverse transformation works similarly

\[ x[n] = \frac{1}{2 \pi N} \sum_{k=0}^{N-1} X[n] e^{i k n / N} \]

## Notes On The DFT

The Xs are still complex (!)

\(x[n]\) is treated as cyclic

The inverse is computed only as the sum of discrete frequencies

The sampling rate is handled here by unit frequency. Frequency needs to be scaled by the sample rate to be meaningful

This math is so full of fiddly that it is really easy to get it screwed up

Frequency resolution is a function of sampled period

The transforms as given are \(O(N^2)\)

The DFT can be thought of as a Gaussian Filter bank

## The Fast Fourier Transform

A dynamic programming trick gets the time complexity of the DFT down to \(O(N \lg N)\)

No, I’m not going to explain it here: treat it as a black box

The FFT gives exactly the same answers as the DFT, just faster

Limitation: The FFT requires a power-of-two number of samples

Limitation: The output frequencies must be linear, which may not be what you wished for

## The Discrete Cosine Transform

Usually you don’t care about phase information

The DCT gives you something similar to the FFT, but only magnitudes

Still can be done \(O(N lg N)\)

The DCT has a different set of boundary conditions

## Spectra of Common Signals

Triangle, square: Fundamental plus “odd harmonics”

Difference between triangle and square is decay rate, phase

Odd harmonics are a characteristic of many kinds of distortion

“White noise” (random samples): Flattish noise spectrum (“noise is a sum of random frequencies”)

## Frequency Localization In Time

Fundamentally ill-defined: FT only works right on an infinite periodic signal, throws away all information about changes in frequency

Heisenbergy: Smaller DFT gives more local information about frequency, but at the cost of poorer accuracy. Low frequency signals, in particular, will not be captured

“Overlapping” heuristic: Do a small DFT, slide over a few samples, do it again

## Windowing

DFT treats input as cyclic: if not analyzing over exactly an integer number of periods, trouble

In particular, a step change corresponds to a smooth spectrum, so endpoints must match (also all derivatives)

“Windowing” heuristic: Taper the time-domain signal off toward the edges

A window corresponds to a low-pass filter: more next lecture

## DFT Application: Frequency Analysis

Pretty straightforward: do the DFT, look for peaks etc in the spectrum

Still have to do the analysis of the spectrum, which involves some kind of other math or heuristics

There are more sensitive frequency analysis methods that do not involve the DFT

## DFT Application: Filtering

Pretty straightforward: do the DFT, adjust frequencies as desired, do the inverse DFT

See localization in time: will end up doing “overlapping windowed” DFT to track changes input frequencies

Expensive compared to “direct” filtering we will discuss next time

## DFT Application: Lossy Compression

Do the DFT, remove “unwanted” stuff

Depends pretty hard on psychoacoustics