Multi-level LSTM framework with hybrid sonic features for human–animal conflict evasion

Abstract

Human–animal conflict (HAC) is one of the main issues that the government of India is now addressing. In this work, we proposed a stacked long short-term memory (LSTM) as well as hybrid features for automatic wild animal detection and state of mind classification based on intelligent perception of the environment. The elephant was the wildlife animal under consideration in this work. This study initially collects the information of wild animals from their environment. We then extracted and combined the mel frequency cepstral coefficient (MFCC), delta MFCC, double delta MFCC, and Linear Predictive Coding (LPC) features in various combinations. This combination of MFCC and its derivatives with LPC provides improved performance. After that, the elephants are identified, and their state of mind (SOM) is classified by utilising the proposed stacked LSTM framework. The results obtained demonstrated that the stacked LSTM framework performed better than both the single LSTM and the bidirectional LSTM learning network. For elephant detection, the classification accuracy obtained was 98%, and for state-of-mind detection, the classification accuracy obtained was 97%. Further, if the presence of elephants is confirmed, it is repelled with the help of an animated predator to scare the animal.