BAD-Classifier/Machine-Learning

Implementation of a CNN to classify South African bird calls # Machine-Learning ### Convolutional Neural Network (CNN) This project uses a CNN to learn the MFCCs for each of the bird species. The CNN architecture is made up of: - conv2d - 32 filters - conv2d - 32 filters - maxpooling of 3 x 3 - conv2d - 64 filters - conv2d - 64 filters - maxpooling 2 x 2 - conv2d - 128 filters - conv2d - 128 filters - maxpooling 2 x 2 - flatten - dense 1024 - dense 10 (for 10 birds) - softmax Each conv2d layer uses relu activations and batch normalization after the activations. Dropout of 50% is used on each conv2d layer while 80% drop is used on the dense 1024 layer. Adam optimizer is used. Data is augmented to increase the sample size and to add some regularization of the data.