# Compute the scattering transform of a speech recording¶

This script loads a speech signal from the free spoken digit dataset (FSDD) of a man pronouncing the word “zero,” computes its scattering transform, and displays the zeroth-, first-, and second-order scattering coefficients.

## Preliminaries¶

Since kymatio handles PyTorch arrays, we first import torch.

```import torch
```

To handle audio file I/O, we import os and scipy.io.wavfile.

```import os
import scipy.io.wavfile
```

We import matplotlib to plot the calculated scattering coefficients.

```import matplotlib.pyplot as plt
```

Finally, we import the Scattering1D class from the scattering package and the fetch_fsdd function from scattering.datasets. The Scattering1D class is what lets us calculate the scattering transform, while the fetch_fsdd function downloads the FSDD, if needed.

```from kymatio import Scattering1D
from kymatio.datasets import fetch_fsdd
```

## Scattering setup¶

First, we make download the FSDD (if not already downloaded) and read in the recording 0_jackson_0.wav of a man pronouncing the word “zero”.

```info_dataset = fetch_fsdd(verbose=True)

file_path = os.path.join(info_dataset['path_dataset'],
sorted(info_dataset['files']))
_, x = scipy.io.wavfile.read(file_path)
```

Once the recording is in memory, we convert it to a PyTorch Tensor, normalize it, and reshape it to the form (B, C, T), where B is the batch size, C is the number of channels, and T is the number of samples in the recording. In our case, we have only one signal in our batch, so B = 1. We also have a single channel, so C = 1. Note that C is almost always 1, for input Tensors as this axis indexes the different scattering coefficients.

```x = torch.from_numpy(x).float()
x /= x.abs().max()
x = x.view(1, -1)
```

We are now ready to set up the parameters for the scattering transform. First, the number of samples, T, is given by the size of our input x. The averaging scale is specified as a power of two, 2**J. Here, we set J = 6 to get an averaging, or maximum, scattering scale of 2**6 = 64 samples. Finally, we set the number of wavelets per octave, Q, to 16. This lets us resolve frequencies at a resolution of 1/16 octaves.

```T = x.shape[-1]
J = 6
Q = 16
```

Finally, we are able to create the object which computes our scattering transform, scattering.

```scattering = Scattering1D(J, T, Q)
```

## Compute and display the scattering coefficients¶

Computing the scattering transform of a PyTorch Tensor is achieved using the forward() method of the Scattering1D class. The output is another Tensor of shape (B, C, T). Here, B is the same as for the input x, but C is the number of scattering coefficient outputs, and T is the number of samples along the time axis. This is typically much smaller than the number of input samples since the scattering transform performs an average in time and subsamples the result to save memory.

```Sx = scattering.forward(x)
```

To display the scattering coefficients, we must first identify which belong to each order (zeroth, first, or second). We do this by extracting the meta information from the scattering object and constructing masks for each order.

```meta = Scattering1D.compute_meta_scattering(J, Q)
order0 = (meta['order'] == 0)
order1 = (meta['order'] == 1)
order2 = (meta['order'] == 2)
```

First, we plot the original signal x. Note that we have to index it as x[0,0,:] to convert it to a one-dimensional array and convert it to a numpy array using the numpy() method.

```plt.figure(figsize=(8, 2))
plt.plot(x[0,:].numpy())
plt.title('Original signal')
``` We now plot the zeroth-order scattering coefficient, which is simply an average of the original signal at the scale 2**J.

```plt.figure(figsize=(8, 2))
plt.plot(Sx[0,order0,:].numpy().ravel())
plt.title('Scattering Order 0')
``` We then plot the first-order coefficients, which are arranged along time and log-frequency.

```plt.figure(figsize=(8, 2))
plt.imshow(Sx[0,order1,:].numpy(), aspect='auto')
plt.title('Scattering Order 1')
``` Finally, we plot the second-order scattering coefficients. These are also organized aling time, but has two log-frequency indices: one first-order frequency and one second-order frequency. Here, both indices are mixed along the vertical axis.

```plt.figure(figsize=(8, 2))
plt.imshow(Sx[0,order2,:].numpy(), aspect='auto')
plt.title('Scattering Order 2')
``` Display the plots!

```plt.show()
```

Total running time of the script: ( 0 minutes 0.658 seconds)

Gallery generated by Sphinx-Gallery