*Observe: Like a number of prior ones, this submit is an excerpt from the forthcoming e-book, Deep Studying and Scientific Computing with R torch. And like many excerpts, it’s a product of exhausting trade-offs. For extra depth and extra examples, I’ve to ask you to please seek the advice of the e-book.*

## Wavelets and the Wavelet Remodel

What are wavelets? Just like the Fourier foundation, they’re features; however they don’t lengthen infinitely. As an alternative, they’re localized in time: Away from the middle, they shortly decay to zero. Along with a *location* parameter, additionally they have a *scale*: At totally different scales, they seem squished or stretched. Squished, they’ll do higher at detecting excessive frequencies; the converse applies after they’re stretched out in time.

The essential operation concerned within the Wavelet Remodel is convolution – have the (flipped) wavelet slide over the info, computing a sequence of dot merchandise. This fashion, the wavelet is mainly in search of *similarity*.

As to the wavelet features themselves, there are a lot of of them. In a sensible software, we’d wish to experiment and choose the one which works finest for the given knowledge. In comparison with the DFT and spectrograms, extra experimentation tends to be concerned in wavelet evaluation.

The subject of wavelets could be very totally different from that of Fourier transforms in different respects, as nicely. Notably, there’s a lot much less standardization in terminology, use of symbols, and precise practices. On this introduction, I’m leaning closely on one particular exposition, the one in Arnt Vistnes’ very good e-book on waves (Vistnes 2018). In different phrases, each terminology and examples replicate the alternatives made in that e-book.

## Introducing the Morlet wavelet

The Morlet, also referred to as Gabor, wavelet is outlined like so:

[

Psi_{omega_{a},K,t_{k}}(t_n) = (e^{-i omega_{a} (t_n – t_k)} – e^{-K^2}) e^{- omega_a^2 (t_n – t_k )^2 /(2K )^2}

]

This formulation pertains to discretized knowledge, the varieties of information we work with in follow. Thus, (t_k) and (t_n) designate cut-off dates, or equivalently, particular person time-series samples.

This equation appears daunting at first, however we will “tame” it a bit by analyzing its construction, and pointing to the primary actors. For concreteness, although, we first have a look at an instance wavelet.

We begin by implementing the above equation:

Evaluating code and mathematical formulation, we discover a distinction. The perform itself takes one argument, (t_n); its realization, 4 (`omega`

, `Ok`

, `t_k`

, and `t`

). It is because the `torch`

code is vectorized: On the one hand, `omega`

, `Ok`

, and `t_k`

, which, within the system, correspond to (omega_{a}), (Ok), and (t_k) , are scalars. (Within the equation, they’re assumed to be fastened.) `t`

, then again, is a vector; it would maintain the measurement instances of the sequence to be analyzed.

We choose instance values for `omega`

, `Ok`

, and `t_k`

, in addition to a variety of instances to judge the wavelet on, and plot its values:

```
omega <- 6 * pi
Ok <- 6
t_k <- 5
sample_time <- torch_arange(3, 7, 0.0001)
create_wavelet_plot <- perform(omega, Ok, t_k, sample_time) {
morlet <- morlet(omega, Ok, t_k, sample_time)
df <- knowledge.body(
x = as.numeric(sample_time),
actual = as.numeric(morlet$actual),
imag = as.numeric(morlet$imag)
) %>%
pivot_longer(-x, names_to = "half", values_to = "worth")
ggplot(df, aes(x = x, y = worth, coloration = half)) +
geom_line() +
scale_colour_grey(begin = 0.8, finish = 0.4) +
xlab("time") +
ylab("wavelet worth") +
ggtitle("Morlet wavelet",
subtitle = paste0("ω_a = ", omega / pi, "π , Ok = ", Ok)
) +
theme_minimal()
}
create_wavelet_plot(omega, Ok, t_k, sample_time)
```

What we see here’s a complicated sine curve – notice the true and imaginary elements, separated by a part shift of (pi/2) – that decays on each side of the middle. Wanting again on the equation, we will determine the elements accountable for each options. The primary time period within the equation, (e^{-i omega_{a} (t_n – t_k)}), generates the oscillation; the third, (e^{- omega_a^2 (t_n – t_k )^2 /(2K )^2}), causes the exponential decay away from the middle. (In case you’re questioning in regards to the second time period, (e^{-Ok^2}): For given (Ok), it’s only a fixed.)

The third time period truly is a Gaussian, with location parameter (t_k) and scale (Ok). We’ll discuss (Ok) in nice element quickly, however what’s with (t_k)? (t_k) is the middle of the wavelet; for the Morlet wavelet, that is additionally the placement of most amplitude. As distance from the middle will increase, values shortly method zero. That is what is supposed by wavelets being localized: They’re “lively” solely on a brief vary of time.

## The roles of (Ok) and (omega_a)

Now, we already mentioned that (Ok) is the size of the Gaussian; it thus determines how far the curve spreads out in time. However there’s additionally (omega_a). Wanting again on the Gaussian time period, it, too, will influence the unfold.

First although, what’s (omega_a)? The subscript (a) stands for “evaluation”; thus, (omega_a) denotes a single frequency being probed.

Now, let’s first examine visually the respective impacts of (omega_a) and (Ok).

```
p1 <- create_wavelet_plot(6 * pi, 4, 5, sample_time)
p2 <- create_wavelet_plot(6 * pi, 6, 5, sample_time)
p3 <- create_wavelet_plot(6 * pi, 8, 5, sample_time)
p4 <- create_wavelet_plot(4 * pi, 6, 5, sample_time)
p5 <- create_wavelet_plot(6 * pi, 6, 5, sample_time)
p6 <- create_wavelet_plot(8 * pi, 6, 5, sample_time)
(p1 | p4) /
(p2 | p5) /
(p3 | p6)
```

Within the left column, we maintain (omega_a) fixed, and fluctuate (Ok). On the proper, (omega_a) adjustments, and (Ok) stays the identical.

Firstly, we observe that the upper (Ok), the extra the curve will get unfold out. In a wavelet evaluation, because of this extra cut-off dates will contribute to the rework’s output, leading to excessive precision as to frequency content material, however lack of decision in time. (We’ll return to this – central – trade-off quickly.)

As to (omega_a), its influence is twofold. On the one hand, within the Gaussian time period, it counteracts – *precisely*, even – the size parameter, (Ok). On the opposite, it determines the frequency, or equivalently, the interval, of the wave. To see this, check out the proper column. Equivalent to the totally different frequencies, we now have, within the interval between 4 and 6, 4, six, or eight peaks, respectively.

This double position of (omega_a) is the explanation why, all-in-all, it *does* make a distinction whether or not we shrink (Ok), preserving (omega_a) fixed, or improve (omega_a), holding (Ok) fastened.

This state of issues sounds difficult, however is much less problematic than it may appear. In follow, understanding the position of (Ok) is necessary, since we have to choose wise (Ok) values to strive. As to the (omega_a), then again, there will likely be a mess of them, similar to the vary of frequencies we analyze.

So we will perceive the influence of (Ok) in additional element, we have to take a primary have a look at the Wavelet Remodel.

## Wavelet Remodel: An easy implementation

Whereas general, the subject of wavelets is extra multifaceted, and thus, could appear extra enigmatic than Fourier evaluation, the rework itself is simpler to understand. It’s a sequence of native convolutions between wavelet and sign. Right here is the system for particular scale parameter (Ok), evaluation frequency (omega_a), and wavelet location (t_k):

[

W_{K, omega_a, t_k} = sum_n x_n Psi_{omega_{a},K,t_{k}}^*(t_n)

]

That is only a dot product, computed between sign and complex-conjugated wavelet. (Right here complicated conjugation flips the wavelet in time, making this *convolution*, not correlation – a incontrovertible fact that issues so much, as you’ll see quickly.)

Correspondingly, simple implementation leads to a sequence of dot merchandise, every similar to a unique alignment of wavelet and sign. Beneath, in `wavelet_transform()`

, arguments `omega`

and `Ok`

are scalars, whereas `x`

, the sign, is a vector. The result’s the wavelet-transformed sign, for some particular `Ok`

and `omega`

of curiosity.

```
wavelet_transform <- perform(x, omega, Ok) {
n_samples <- dim(x)[1]
W <- torch_complex(
torch_zeros(n_samples), torch_zeros(n_samples)
)
for (i in 1:n_samples) {
# transfer heart of wavelet
t_k <- x[i, 1]
m <- morlet(omega, Ok, t_k, x[, 1])
# compute native dot product
# notice wavelet is conjugated
dot <- torch_matmul(
m$conj()$unsqueeze(1),
x[, 2]$to(dtype = torch_cfloat())
)
W[i] <- dot
}
W
}
```

To check this, we generate a easy sine wave that has a frequency of 100 Hertz in its first half, and double that within the second.

```
gencos <- perform(amp, freq, part, fs, period) {
x <- torch_arange(0, period, 1 / fs)[1:-2]$unsqueeze(2)
y <- amp * torch_cos(2 * pi * freq * x + part)
torch_cat(record(x, y), dim = 2)
}
# sampling frequency
fs <- 8000
f1 <- 100
f2 <- 200
part <- 0
period <- 0.25
s1 <- gencos(1, f1, part, fs, period)
s2 <- gencos(1, f2, part, fs, period)
s3 <- torch_cat(record(s1, s2), dim = 1)
s3[(dim(s1)[1] + 1):(dim(s1)[1] * 2), 1] <-
s3[(dim(s1)[1] + 1):(dim(s1)[1] * 2), 1] + period
df <- knowledge.body(
x = as.numeric(s3[, 1]),
y = as.numeric(s3[, 2])
)
ggplot(df, aes(x = x, y = y)) +
geom_line() +
xlab("time") +
ylab("amplitude") +
theme_minimal()
```

Now, we run the Wavelet Remodel on this sign, for an evaluation frequency of 100 Hertz, and with a `Ok`

parameter of two, discovered via fast experimentation:

```
Ok <- 2
omega <- 2 * pi * f1
res <- wavelet_transform(x = s3, omega, Ok)
df <- knowledge.body(
x = as.numeric(s3[, 1]),
y = as.numeric(res$abs())
)
ggplot(df, aes(x = x, y = y)) +
geom_line() +
xlab("time") +
ylab("Wavelet Remodel") +
theme_minimal()
```

The rework appropriately picks out the a part of the sign that matches the evaluation frequency. In the event you really feel like, you may wish to double-check what occurs for an evaluation frequency of 200 Hertz.

Now, in actuality we’ll wish to run this evaluation not for a single frequency, however a variety of frequencies we’re serious about. And we’ll wish to strive totally different scales `Ok`

. Now, for those who executed the code above, you is perhaps apprehensive that this might take a *lot* of time.

Effectively, it by necessity takes longer to compute than its Fourier analogue, the spectrogram. For one, that’s as a result of with spectrograms, the evaluation is “simply” two-dimensional, the axes being time and frequency. With wavelets there are, as well as, totally different scales to be explored. And secondly, spectrograms function on complete home windows (with configurable overlap); a wavelet, then again, slides over the sign in unit steps.

Nonetheless, the state of affairs just isn’t as grave because it sounds. The Wavelet Remodel being a *convolution*, we will implement it within the Fourier area as an alternative. We’ll try this very quickly, however first, as promised, let’s revisit the subject of various `Ok`

.

## Decision in time versus in frequency

We already noticed that the upper `Ok`

, the extra spread-out the wavelet. We are able to use our first, maximally simple, instance, to analyze one speedy consequence. What, for instance, occurs for `Ok`

set to twenty?

```
Ok <- 20
res <- wavelet_transform(x = s3, omega, Ok)
df <- knowledge.body(
x = as.numeric(s3[, 1]),
y = as.numeric(res$abs())
)
ggplot(df, aes(x = x, y = y)) +
geom_line() +
xlab("time") +
ylab("Wavelet Remodel") +
theme_minimal()
```

The Wavelet Remodel nonetheless picks out the right area of the sign – however now, as an alternative of a rectangle-like consequence, we get a considerably smoothed model that doesn’t sharply separate the 2 areas.

Notably, the primary 0.05 seconds, too, present appreciable smoothing. The bigger a wavelet, the extra element-wise merchandise will likely be misplaced on the finish and the start. It is because transforms are computed aligning the wavelet in any respect sign positions, from the very first to the final. Concretely, after we compute the dot product at location `t_k = 1`

, only a single pattern of the sign is taken into account.

Aside from probably introducing unreliability on the boundaries, how does wavelet scale have an effect on the evaluation? Effectively, since we’re *correlating* (*convolving*, technically; however on this case, the impact, in the long run, is identical) the wavelet with the sign, point-wise similarity is what issues. Concretely, assume the sign is a pure sine wave, the wavelet we’re utilizing is a windowed sinusoid just like the Morlet, and that we’ve discovered an optimum `Ok`

that properly captures the sign’s frequency. Then another `Ok`

, be it bigger or smaller, will lead to much less point-wise overlap.

## Performing the Wavelet Remodel within the Fourier area

Quickly, we’ll run the Wavelet Remodel on an extended sign. Thus, it’s time to pace up computation. We already mentioned that right here, we profit from time-domain convolution being equal to multiplication within the Fourier area. The general course of then is that this: First, compute the DFT of each sign and wavelet; second, multiply the outcomes; third, inverse-transform again to the time area.

The DFT of the sign is shortly computed:

`F <- torch_fft_fft(s3[ , 2])`

With the Morlet wavelet, we don’t even need to run the FFT: Its Fourier-domain illustration may be acknowledged in closed kind. We’ll simply make use of that formulation from the outset. Right here it’s:

```
morlet_fourier <- perform(Ok, omega_a, omega) {
2 * (torch_exp(-torch_square(
Ok * (omega - omega_a) / omega_a
)) -
torch_exp(-torch_square(Ok)) *
torch_exp(-torch_square(Ok * omega / omega_a)))
}
```

Evaluating this assertion of the wavelet to the time-domain one, we see that – as anticipated – as an alternative of parameters `t`

and `t_k`

it now takes `omega`

and `omega_a`

. The latter, `omega_a`

, is the evaluation frequency, the one we’re probing for, a scalar; the previous, `omega`

, the vary of frequencies that seem within the DFT of the sign.

In instantiating the wavelet, there’s one factor we have to pay particular consideration to. In FFT-think, the frequencies are bins; their quantity is decided by the size of the sign (a size that, for its half, straight is determined by sampling frequency). Our wavelet, then again, works with frequencies in Hertz (properly, from a consumer’s perspective; since this unit is significant to us). What this implies is that to `morlet_fourier`

, as `omega_a`

we have to go not the worth in Hertz, however the corresponding FFT bin. Conversion is completed relating the variety of bins, `dim(x)[1]`

, to the sampling frequency of the sign, `fs`

:

```
# once more search for 100Hz elements
omega <- 2 * pi * f1
# want the bin similar to some frequency in Hz
omega_bin <- f1/fs * dim(s3)[1]
```

We instantiate the wavelet, carry out the Fourier-domain multiplication, and inverse-transform the consequence:

```
Ok <- 3
m <- morlet_fourier(Ok, omega_bin, 1:dim(s3)[1])
prod <- F * m
reworked <- torch_fft_ifft(prod)
```

Placing collectively wavelet instantiation and the steps concerned within the evaluation, we now have the next. (Observe how you can `wavelet_transform_fourier`

, we now, conveniently, go within the frequency worth in Hertz.)

```
wavelet_transform_fourier <- perform(x, omega_a, Ok, fs) {
N <- dim(x)[1]
omega_bin <- omega_a / fs * N
m <- morlet_fourier(Ok, omega_bin, 1:N)
x_fft <- torch_fft_fft(x)
prod <- x_fft * m
w <- torch_fft_ifft(prod)
w
}
```

We’ve already made vital progress. We’re prepared for the ultimate step: automating evaluation over a variety of frequencies of curiosity. It will lead to a three-dimensional illustration, the wavelet diagram.

## Creating the wavelet diagram

Within the Fourier Remodel, the variety of coefficients we get hold of is determined by sign size, and successfully reduces to half the sampling frequency. With its wavelet analogue, since anyway we’re doing a loop over frequencies, we’d as nicely resolve which frequencies to research.

Firstly, the vary of frequencies of curiosity may be decided operating the DFT. The following query, then, is about granularity. Right here, I’ll be following the advice given in Vistnes’ e-book, which relies on the relation between present frequency worth and wavelet scale, `Ok`

.

Iteration over frequencies is then carried out as a loop:

```
wavelet_grid <- perform(x, Ok, f_start, f_end, fs) {
# downsample evaluation frequency vary
# as per Vistnes, eq. 14.17
num_freqs <- 1 + log(f_end / f_start)/ log(1 + 1/(8 * Ok))
freqs <- seq(f_start, f_end, size.out = flooring(num_freqs))
reworked <- torch_zeros(
num_freqs, dim(x)[1],
dtype = torch_cfloat()
)
for(i in 1:num_freqs) {
w <- wavelet_transform_fourier(x, freqs[i], Ok, fs)
reworked[i, ] <- w
}
record(reworked, freqs)
}
```

Calling `wavelet_grid()`

will give us the evaluation frequencies used, along with the respective outputs from the Wavelet Remodel.

Subsequent, we create a utility perform that visualizes the consequence. By default, `plot_wavelet_diagram()`

shows the magnitude of the wavelet-transformed sequence; it may well, nevertheless, plot the squared magnitudes, too, in addition to their sq. root, a technique a lot really useful by Vistnes whose effectiveness we’ll quickly have alternative to witness.

The perform deserves a number of additional feedback.

Firstly, identical as we did with the evaluation frequencies, we down-sample the sign itself, avoiding to counsel a decision that isn’t truly current. The system, once more, is taken from Vistnes’ e-book.

Then, we use interpolation to acquire a brand new time-frequency grid. This step could even be crucial if we maintain the unique grid, since when distances between grid factors are very small, R’s `picture()`

could refuse to simply accept axes as evenly spaced.

Lastly, notice how frequencies are organized on a log scale. This results in way more helpful visualizations.

```
plot_wavelet_diagram <- perform(x,
freqs,
grid,
Ok,
fs,
f_end,
kind = "magnitude") {
grid <- change(kind,
magnitude = grid$abs(),
magnitude_squared = torch_square(grid$abs()),
magnitude_sqrt = torch_sqrt(grid$abs())
)
# downsample time sequence
# as per Vistnes, eq. 14.9
new_x_take_every <- max(Ok / 24 * fs / f_end, 1)
new_x_length <- flooring(dim(grid)[2] / new_x_take_every)
new_x <- torch_arange(
x[1],
x[dim(x)[1]],
step = x[dim(x)[1]] / new_x_length
)
# interpolate grid
new_grid <- nnf_interpolate(
grid$view(c(1, 1, dim(grid)[1], dim(grid)[2])),
c(dim(grid)[1], new_x_length)
)$squeeze()
out <- as.matrix(new_grid)
# plot log frequencies
freqs <- log10(freqs)
picture(
x = as.numeric(new_x),
y = freqs,
z = t(out),
ylab = "log frequency [Hz]",
xlab = "time [s]",
col = hcl.colours(12, palette = "Mild grays")
)
important <- paste0("Wavelet Remodel, Ok = ", Ok)
sub <- change(kind,
magnitude = "Magnitude",
magnitude_squared = "Magnitude squared",
magnitude_sqrt = "Magnitude (sq. root)"
)
mtext(facet = 3, line = 2, at = 0, adj = 0, cex = 1.3, important)
mtext(facet = 3, line = 1, at = 0, adj = 0, cex = 1, sub)
}
```

Let’s use this on a real-world instance.

## An actual-world instance: Chaffinch’s music

For the case examine, I’ve chosen what, to me, was essentially the most spectacular wavelet evaluation proven in Vistnes’ e-book. It’s a pattern of a chaffinch’s singing, and it’s obtainable on Vistnes’ web site.

```
url <- "http://www.physics.uio.no/pow/wavbirds/chaffinch.wav"
obtain.file(
file.path(url),
destfile = "/tmp/chaffinch.wav"
)
```

We use `torchaudio`

to load the file, and convert from stereo to mono utilizing `tuneR`

’s appropriately named `mono()`

. (For the form of evaluation we’re doing, there isn’t a level in preserving two channels round.)

```
Wave Object
Variety of Samples: 1864548
Length (seconds): 42.28
Samplingrate (Hertz): 44100
Channels (Mono/Stereo): Mono
PCM (integer format): TRUE
Bit (8/16/24/32/64): 16
```

For evaluation, we don’t want the entire sequence. Helpfully, Vistnes additionally printed a advice as to which vary of samples to research.

```
waveform_and_sample_rate <- transform_to_tensor(wav)
x <- waveform_and_sample_rate[[1]]$squeeze()
fs <- waveform_and_sample_rate[[2]]
# http://www.physics.uio.no/pow/wavbirds/chaffinchInfo.txt
begin <- 34000
N <- 1024 * 128
finish <- begin + N - 1
x <- x[start:end]
dim(x)
```

`[1] 131072`

How does this look within the time area? (Don’t miss out on the event to really *hear* to it, in your laptop computer.)

```
df <- knowledge.body(x = 1:dim(x)[1], y = as.numeric(x))
ggplot(df, aes(x = x, y = y)) +
geom_line() +
xlab("pattern") +
ylab("amplitude") +
theme_minimal()
```

Now, we have to decide an affordable vary of study frequencies. To that finish, we run the FFT:

On the x-axis, we plot frequencies, not pattern numbers, and for higher visibility, we zoom in a bit.

```
bins <- 1:dim(F)[1]
freqs <- bins / N * fs
# the bin, not the frequency
cutoff <- N/4
df <- knowledge.body(
x = freqs[1:cutoff],
y = as.numeric(F$abs())[1:cutoff]
)
ggplot(df, aes(x = x, y = y)) +
geom_col() +
xlab("frequency (Hz)") +
ylab("magnitude") +
theme_minimal()
```

Based mostly on this distribution, we will safely prohibit the vary of study frequencies to between, roughly, 1800 and 8500 Hertz. (That is additionally the vary really useful by Vistnes.)

First, although, let’s anchor expectations by making a spectrogram for this sign. Appropriate values for FFT dimension and window dimension had been discovered experimentally. And although, in spectrograms, you don’t see this achieved typically, I discovered that displaying sq. roots of coefficient magnitudes yielded essentially the most informative output.

```
fft_size <- 1024
window_size <- 1024
energy <- 0.5
spectrogram <- transform_spectrogram(
n_fft = fft_size,
win_length = window_size,
normalized = TRUE,
energy = energy
)
spec <- spectrogram(x)
dim(spec)
```

`[1] 513 257`

Like we do with wavelet diagrams, we plot frequencies on a log scale.

```
bins <- 1:dim(spec)[1]
freqs <- bins * fs / fft_size
log_freqs <- log10(freqs)
frames <- 1:(dim(spec)[2])
seconds <- (frames / dim(spec)[2]) * (dim(x)[1] / fs)
picture(x = seconds,
y = log_freqs,
z = t(as.matrix(spec)),
ylab = 'log frequency [Hz]',
xlab = 'time [s]',
col = hcl.colours(12, palette = "Mild grays")
)
important <- paste0("Spectrogram, window dimension = ", window_size)
sub <- "Magnitude (sq. root)"
mtext(facet = 3, line = 2, at = 0, adj = 0, cex = 1.3, important)
mtext(facet = 3, line = 1, at = 0, adj = 0, cex = 1, sub)
```

The spectrogram already reveals a particular sample. Let’s see what may be achieved with wavelet evaluation. Having experimented with a number of totally different `Ok`

, I agree with Vistnes that `Ok = 48`

makes for a superb alternative:

The acquire in decision, on each the time and the frequency axis, is totally spectacular.

Thanks for studying!

Picture by Vlad Panov on Unsplash

Vistnes, Arnt Inge. 2018. *Physics of Oscillations and Waves. With Use of Matlab and Python*. Springer.