Digit Recognition from Sound

A simple neural network (CNN) to classify spoken digits (0-9).

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Dataset: free-spoken-digit-dataset (FSDD)

Step 1 - Data Preprocessing

The data is provided as 50 audio samples (WAV files) of each digit per person, and 3 people have contributed to the official project.

Total data = 1500 audio samples in .wav format.

We split the data as 90% train - 10% test.

Possible approaches to this problem -

• Simple Neural Network
  

  1. Load the wav file as a NUMPY array, and feed this numpy array to a simple multi-layer perceptron.
  2. When we convert a WAV file to a NUMPY array, the data gets stored as a 1-D matrix. The length of this array is not specific, andit is highly dependent on the data.
  3. Due to this, the first layer of the MLP will have ~10000 neurons. Further calculations will add extreme complexity.

• Spectrogram
  

  1. Convert the wav data into a spectrogram (image file) of size (64*64)
  2. Feed the image file to a simple Neural Network with 4096 neurons in the first layer.
  3. This is a good approach, but the number of neurons are large, and it does not seem logical to flatten out an image and feed it to a simple NN. We can do better.
  4. Based on point 3 above, we can feed this 64*64 image to a simple Convolutional Neural Network.
  5. Every audio willl be converted into a simple 2-D image, and this image will be fed to a CNN. This will speed up the training, and as CNNs are flawless in simple image recognition, we will definitely get a good output.

Spectrogram

• Mel-Frequency Cepstrum Coefficient
  Here’s what Wikipedia has to say about MFCC -
  
  In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC. They are derived from a type of cepstral representation of the audio clip (a nonlinear “spectrum-of-a-spectrum”). The difference between the cepstrum and the mel-frequency cepstrum is that in the MFC, the frequency bands are equally spaced on the mel scale, which approximates the human auditory system’s response more closely than the linearly-spaced frequency bands used in the normal cepstrum. This frequency warping can allow for better representation of sound, for example, in audio compression.

  1. MFCC is a better representation of sound. And it can be treated like an image for all practical purposes for training.
  2. Thus, I think using the MFCC representation of the WAV files is the best approach.

We move forward with the MFCC approach

Step 2 - Model Building

We use Keras for the model building.

• Model Hyperparameters
  
  1. Optimizer - Adadelta
     
  2. Activation - ReLU
     
  3. Number of epochs - 50
     
  4. Batch Size - 64
     
  5. Learning rate - Adadelta default
     
  6. Loss - Categorical Crossentropy
     
• Model Structure
  
  1. 3 convolutional layers
  2. 1 Max Pooling Layer
  3. 3 dense layers (MLP)
  4. Softmax Activation for output
  5. BatchNormalization Layer after every Conv Layer and Dense Layer.
  6. Dropout for every layer of MLP.

Tensorboard Visualisation of Model

Step 3 - Training

• Accuracy
  

Model Accuracy

• Loss
  

Model Loss

We get 98% validation accuracy!

Step 4 - Test

We use the test data to check the model performance on new data. Based on the results, we get 97% accuracy!

         precision    recall  f1-score   support

      0       1.00      0.84      0.91        19
      1       0.87      0.87      0.87        15
      2       1.00      1.00      1.00        23
      3       0.91      1.00      0.95        10
      4       1.00      1.00      1.00        10
      5       1.00      1.00      1.00        23
      6       1.00      1.00      1.00        13
      7       0.93      1.00      0.96        13
      8       1.00      1.00      1.00        14
      9       0.91      1.00      0.95        10

avg / total       0.97      0.97      0.97       150

We have thus trained a Neural Network to correctly classify spoken digits.