Typical workflows

In this section, we'll demonstrate some common ways of interacting with the package

To quickly try things out, you can generate some example models of signals using examplemodels like this models = examplemodels(10).

Calculate root embeddings from sound files

In this example, we'll read a bunch of sound files and calculate embedding vectors containing information about the poles of estimated rational spectra. These embeddings are useful for classification etc. See the paper for further explanation.

This example makes use of a few other packages, notably AudioClustering.jl for some convenience functions.

using Glob, WAV, SpectralDistances
const fs = 44100
using Grep
using DSP, LPVSpectral
using AudioClustering

path = "path/to/folder/with/wav-files"

files     = glob("*.wav")
labels0   = match.(r"[a-z_]+", files)..:match .|> String # This regex assumes that the files are named in a certain way, you may adopt as needed, or load the labels separately.
ulabels   = unique(labels0)
n_classes = length(ulabels)
labels    = sum((labels0 .== reshape(ulabels,1,:)) .* (1:n_classes)', dims=2)[:]
na        = 18 # Order of the models (prefer an even number)
fitmethod = LS(na=na, λ=1e-5)
fs        = 44100 # the sample rate

models = mapsoundfiles(files, fs) do sound # mapsoundfiles is defined in AudioClustering
    sound = SpectralDistances.bp_filter(sound, (50/fs, 18000/fs)) # prefiltering is a good idea
    SpectralDistances.fitmodel(fitmethod, sound)

X = embeddings(models)

We now have a matrix X with features, we can run clustering on it like this:

using Clustering
labels,models,X,Z = get_features(trainpath)
cr = kmeans(v1(X,2), n_classes) # v1 normalizes mean and variance

        marker_z          = labels,
        m                 = (2, 0.5),
        markerstrokealpha = 0,
        colorbar          = false,
        title             = "Correct assignment",
        marker_z          = cr.assignments,
        m                 = (2, 0.5),
        markerstrokealpha = 0,
        colorbar          = false,
        title             = "K-means on w assignment",
    legend = false,

Another clustering approach is to use kbarycenters, see example K-Barycenters.

Nearest Neighbor classification

Using embeddings

Here, we will classify a signal based on it's nearest neighbor in a training dataset. The example assumes that the matrix X from the previous example is available, and that there is a similar matrix Xt created from a test dataset. We will classify the entries in the test set using the entries in the training set. The example also assumes that there are two vectors labels::Vector{Int} and labelst::Vector{Int} that contain the class labels.

using AMD # For permutation of the confusion matrix to more easily identity similar classes.
using MultivariateStats # For whitening transform

function knn_classify(labels, X, Xt, k)
    N    = size(Xt,2)
    y    = zeros(Int, N)
    W    = fit(Whitening, X)
    X    = MultivariateStats.transform(W,X)
    Xt   = MultivariateStats.transform(W,Xt)
    tree = NearestNeighbors.KDTree(X)
    for i in 1:N
        inds, dists = knn(tree, Xt[:,i], k)
        y[i]        = mode(labels[inds])

yht = knn_classify(labels,X,Xt,1) # look at the single nearest neighbor
@show mean(labelst .== yht) # This is the accuracy
cm   = confusmat(30,labelst,yht)
perm = amd(sparse(cm))
cm   = cm[perm,perm]
heatmap(cm./sum(cm,dims=2), xlabel="Predicted class",ylabel="True class", title="Confusion Matrix for Test Data")
anns = [(reverse(ci.I)..., text(val,8)) for (ci,val) in zip(CartesianIndices(cm)[:], vec(cm))]

Using spectrograms

See AudioClustering docs for instructions on how to preprocess data for spectrogram-based clustering. When a number of spectrogram patterns are available, the following function may be used to classify spectrograms in X according to patterns.

A suitable distance for this classification is

using DynamicAxisWarping
normalizer = NormNormalizer
d = DTW(radius=4, dist = Euclidean(), transportcost=1.005, normalizer=normalizer)

When a number of spectrogram patterns are available, the following function may be used to classify spectrograms in X according to patterns.

@time labels, D = AudioClustering.pattern_classify(d, patterns, X)

Pairwise distance matrix

Many algorithms make use of a matrix containing all pairwise distances between points. Given a set of models, we can easily obtain such a matrix:

distance = OptimalTransportRootDistance(domain=Continuous())
D = SpectralDistances.distmat(distance, models, normalize=true)

with this matrix, we can, for instance, run clustering:

using Clustering
cr = hclust(Symmetric(sqrt.(D)))
assignments = cutree(cr,k=30) # k is desired number of clusters

Another clustering approach is to use kbarycenters, see example in the docstring.

Fill in an uncomplete distance matrix

The function complete_distmat takes a distance matrix with missing entries and reconstructs the full matrix. This can be useful if the distances in D are noisy and not all of them are available, such as in a microphone calibration problem.

Detection using examples

A measure of distance can be used for detection, by selecting a few positive examples and calculating the distance to the nearest neighbor within these examples from a new query point, a simple example:

function scorefunction(query_model)
    distance = OptimalTransportRootDistance(domain=Continuous())
    distance_vector = distance.(Ref(query_model),positive_example_models)
    score = minimum(distance_vector)

This can be made significantly more effective (but less accurate) using the knn approach from the Nearest Neighbor classification. If you want to detect a small number of patterns in a much longer signal, see the method using SlidingDistancesBase.distance_profile below.

Computing a spectrogram distance profile

In this example, we'll search through a long spectrogram Y for a short query Q. We will compute a distance profile, which is a vector with all distance between Q and each window into Y of the same length as Q. The distance profile should have minima roughly where Y has the same frequencies as the query, and the global minimum where the chirp has increasing frequency (you can zoon into the figure to verify).

To make computing distance profiles faster, SlidingDistancesBase.distance_profile accepts a stride. We also set β relatively high to get a smooth and noise-free distance profile.

using SpectralDistances, DSP, Plots
Q = spectrogram(
    sin.(LinRange(0.9, 1.1, 10000).^ 2 .* (1:10000)) .+ 0.1 * randn(10000),
    window = hanning,
Y = spectrogram(
    sin.(LinRange(0.7, 1.6, 300_000).^ 2 .* (1:300_000)) .+ 0.01 * randn(300_000),
    window = hanning,

d = ConvOptimalTransportDistance(β=0.05, dynamic_floor=-3.0)
D = distance_profile(d, Q, Y, tol=1e-6, stride=15)
    plot(Y, title = "Data"),
    plot(Q, title = "Query spectrogram"),
    plot(D, title = "Distance Profile"),
    legend = false,
    colorbar = false,

To illustrate the effect of setting invariant_axis = 2, i.e., configuring the distance to be approximately invariant to translations along the time axis, we conpute two different distance profiles

using SpectralDistances, DSP, Plots
N = 48_000
g(x,N) = exp(-10*(x-N/2)^2/N^2)
t = 1:N
f = range(0.01, stop=1, length=N)
y = sin.(t .* f) .* g.(t, N)
y1 = y .+ 0.1 .* randn.()
y2 = [0y; y; 0y] .+ 0.1 .* randn.()

Q = spectrogram(y1, 512, window = hanning)
Y = spectrogram(y2, 512, window = hanning)

d = ConvOptimalTransportDistance(β=0.01, dynamic_floor=-3.0)
D = distance_profile(d, Q, Y, tol=1e-6, stride=15)

di = ConvOptimalTransportDistance(β=0.01, dynamic_floor=-3.0, invariant_axis=2)
Di = distance_profile(di, Q, Y, tol=1e-6, stride=15)

    plot(Y, title = "Data", legend = false, xlabel=""),
    plot(Q, title = "Query spectrogram", legend = false, xlabel=""),
    plot([D Di], title = "Distance Profile", lab=["Regular" "Invariant time axis"]),
    colorbar = false,
    layout = (1,3)

It should be obvious from the distance profiles that the one corresponding to the distance with an invariant axis is less sensitve to purturbations along the time axis. Both distance profiles should have roughly the same global minimum, and they should also be similar when there is no overlap between Q and Y. However, when there's a partial overlap, the invariant distance is smaller.

The closed-form solution

In this example we will simply visualize two spectra, the locations of their poles and the cumulative spectrum functions.

using ControlSystemsBase, SpectralDistances, Plots

G1   = tf(1,[1,0.12,1])*tf(1,[1,0.1,0.1])
G2   = tf(1,[1,0.12,2])*tf(1,[1,0.1,0.4])
a1   = denvec(G1)[]
a2   = denvec(G2)[]
n    = length(a1)

f1c  = w -> abs2(1/sum(j->a1[j]*(im*w)^(n-j), 1:n))
f2c  = w -> abs2(1/sum(j->a2[j]*(im*w)^(n-j), 1:n))
sol1 = SpectralDistances.c∫(f1c,0,3π)
sol2 = SpectralDistances.c∫(f2c,0,3π)

fig1 = plot((sol1.t .+ sol1.t[2]).*2π, sqrt.(sol1 ./ sol1[end]).u, fillrange=sqrt.(sol2(sol1.t) ./ sol2[end]).u, fill=(0.6,:purple), l=(2,:blue))
plot!((sol2.t .+ sol2.t[2]).*2π, sqrt.(sol2(sol2.t) ./ sol2[end]).u, l=(2,:orange), xscale=:log10, legend=false, grid=false, xlabel="Frequency", xlims=(1e-2,2pi))

fig2 = bodeplot([G1, G2], exp10.(LinRange(-1.5, 1, 200)), legend=false, grid=false, title="", linecolor=[:blue :orange], l=(2,), plotphase=false)

fig3 = pzmap([G1, G2], legend=false, grid=false, title="", markercolor=[:blue :orange], color=[:blue :orange], m=(2,:c), xlims=(-0.5,0.5))
vline!([0], l=(:black, :dash))
hline!([0], l=(:black, :dash))

plot(fig1, fig2, fig3, layout=(1,3))
┌ Warning: Spectral integration failed, increasing precision
└ @ SpectralDistances ~/work/SpectralDistances.jl/SpectralDistances.jl/src/losses.jl:829
┌ Warning: Spectral integration failed, increasing precision
└ @ SpectralDistances ~/work/SpectralDistances.jl/SpectralDistances.jl/src/losses.jl:829