# Classification of spoken digit recordings¶

In this example we use the 1D scattering transform to represent spoken digits, which we then classify using a simple classifier. This shows that 1D scattering representations are useful for this type of problem.

This dataset is automatically downloaded and preprocessed from https://github.com/Jakobovski/free-spoken-digit-dataset.git

Results: Training accuracy = 99.7% Testing accuracy = 98.0%

## Preliminaries¶

Since we’re using TensorFlow and Keras to train the model, import the relevant modules.

```import tensorflow as tf

from tensorflow.keras import layers
```

To handle audio file I/O, we import os and scipy.io.wavfile. We also need numpy for some basic array manipulation.

```from scipy.io import wavfile
import os
import numpy as np
```

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

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

## Pipeline setup¶

We start by specifying the dimensions of our processing pipeline along with some other parameters.

First, we have signal length. Longer signals are truncated and shorter signals are zero-padded. The sampling rate is 8000 Hz, so this corresponds to little over a second.

```T = 2 ** 13
```

Maximum scale 2**J of the scattering transform (here, about 30 milliseconds) and the number of wavelets per octave.

```J = 8
Q = 12
```

We need a small constant to add to the scattering coefficients before computing the logarithm. This prevents very large values when the scattering coefficients are very close to zero.

```log_eps = 1e-6
```

Once the parameter are set, we can start loading the data into a format that can be fed into the scattering transform and then a logistic regression classifier.

We first download the dataset. If it’s already downloaded, fetch_fsdd will simply return the information corresponding to the dataset that’s already on disk.

```info_data = fetch_fsdd()
files = info_data['files']
path_dataset = info_data['path_dataset']
```

Set up NumPy arrays to hold the audio signals (x_all), the labels (y_all), and whether the signal is in the train or test set (subset).

```x_all = np.zeros((len(files), T))
y_all = np.zeros(len(files), dtype=np.uint8)
subset = np.zeros(len(files), dtype=np.uint8)
```

For each file in the dataset, we extract its label y and its index from the filename. If the index is between 0 and 4, it is placed in the test set, while files with larger indices are used for training. The actual signals are normalized to have maximum amplitude one, and are truncated or zero-padded to the desired length T. They are then stored in the x_all array while their labels are in y_all.

```for k, f in enumerate(files):
basename = f.split('.')

# Get label (0-9) of recording.
y = int(basename.split('_'))

# Index larger than 5 gets assigned to training set.
if int(basename.split('_')) >= 5:
subset[k] = 0
else:
subset[k] = 1

# Load the audio signal and normalize it.
_, x = wavfile.read(os.path.join(path_dataset, f))
x = np.asarray(x, dtype='float')
x /= np.max(np.abs(x))

# If it's too long, truncate it.
if len(x) > T:
x = x[:T]

# If it's too short, zero-pad it.
start = (T - len(x)) // 2

x_all[k,start:start + len(x)] = x
y_all[k] = y
```

## Log-scattering layer¶

We now create a classification model using the Scattering1D Keras layer. First, we take the input signals of length T.

```x_in = layers.Input(shape=(T))
```

These are fed into the Scattering1D layer.

```x = Scattering1D(J, Q=Q)(x_in)
```

Since it does not carry useful information, we remove the zeroth-order scattering coefficients, which are always placed in the first channel of the scattering transform.

```x = layers.Lambda(lambda x: x[..., 1:, :])(x)

# To increase discriminability, we take the logarithm of the scattering
# coefficients (after adding a small constant to make sure nothing blows up
# when scattering coefficients are close to zero). This is known as the
# log-scattering transform.

x = layers.Lambda(lambda x: tf.math.log(tf.abs(x) + log_eps))(x)
```

We then average along the last dimension (time) to get a time-shift invariant representation.

```x = layers.GlobalAveragePooling1D(data_format='channels_first')(x)
```

Finally, we apply batch normalization to ensure that the data is within a moderate range.

```x = layers.BatchNormalization(axis=1)(x)
```

These features are then used to classify the input signal using a dense layer followed by a softmax activation.

```x_out = layers.Dense(10, activation='softmax')(x)
```

Finally, we create the model and display it.

```model = tf.keras.models.Model(x_in, x_out)
model.summary()
```

## Training the classifier¶

Having set up the model, we attach an Adam optimizer and a cross-entropy loss function.

```model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
```

We then train the model using model.fit. The training data is given by those indices satisfying subset == 0.

```model.fit(x_all[subset == 0], y_all[subset == 0], epochs=50,
batch_size=64, validation_split=0.2)
```

Finally, we evaluate the model on the held-out test data. These are given by the indices subset == 1.

```model.evaluate(x_all[subset == 1], y_all[subset == 1], verbose=2)
```

Total running time of the script: ( 0 minutes 0.000 seconds)

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