Binary classification of audio recordings: sounds of rain and animals. The notebook "start" contains primary data visualization, Fourier transforms and converting sound files to their spectrogram. Two neural network architectures are implemented: 1d - CNN and 2d - CNN. The framework used is tensorflow.
The main goal of this project was to build an Artificial Neural Network model with limited amount of sound data of various endangered animal species. The model can be further improved and can be used to located certain animal species in the wild.
Proposed a system which classifies animal sound using a deep convolutional neural network. This repo contains animal sounds used in this work.
Area : Combination of machine learning and embedded hardware design Tools used: Raspberry Pi, Microphone, numpy, Scipy, Wi-fi Module We (team of 4) developed a prototype of a embedded application which could be fitted on a safari vehicle. When the safari goes in the jungle for a ride, if it detects sound, it takes a short sample and tries to classify it according to a pre-trained prediction model. The machine learning algorithm used was random forest. We were successfully able to train the model on lion , tiger, peocock, wolf , elephant and several more such wild animals. The sound samples were obtained from several animal sound repositories. The model achieved an accuracy of around 87% on the test data surmounting problems like noise in sound files, lack of extensive training examples. We also used a Wi-fi module so that information about the animal detected can be broadcast to the tourists' mobile devices.
