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Evaluating the impact of data augmentation and linear/logarithmic scaled amplitude spectrograms on performance of CNN model for capuchinbird audio classification.

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Capuchinbird-Acoustic-Classification

Objective

Built a CNN architecture to test/evaluate the impact of raw data augmentation techniques and different amplitude scaling methods for spectrograms. There have been 4 different models developed which can be differentiated on the following basis :

  1. Inclusion/Exclusion of data augmentation
  2. Amplitude scaling used for spectrograms
Model-Name Augmentation Amplitude Scaling
LiSP false linear
LiSP_AUG true linear
LoSP false logarithmic
LoSP_AUG true logarithmic

The underlying architecture for all the 4 models is the same.

Architecture

The CNN model can be subdivided into two parts:

Feature extraction layer

  • This sub-architecture deals with feature identification part.
  • It consists of two stacked layers, each layer consisting of a Conv2D layer stacked with a pooling layer.
  • The former stacked layer consists of a conv2D layer of 64 kernels connected to an average pooling layer, whereas the latter stacked layer consists of a conv2D layer of 32 kernels connected to a Max Pooling layer.
  • Each convo2D layer uses a ReLU activation function.

Fully connected layer

  • This sub-architecture's job is to classify the identified features into capuchinbird calls or non_capuchinbird calls.
  • It consists of two dense layers of 64 and 32 neurons, respectively.
  • These neurons are interconnected with each other.
  • The final output layer consists of a single neuron using sigmoid activation function.

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Evaluating the impact of data augmentation and linear/logarithmic scaled amplitude spectrograms on performance of CNN model for capuchinbird audio classification.

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