Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation.Developed a deep leaning algorithm which detects anomaly in acoustic sensor data with approx. 90% accuracy.  Implemented the different machine/deep learning algorithms like SVM, KNN, K-means, CNN, Delayed LSTM, Conv LSTM and different Beamforming algorithms such as delay and sum beamforming, linear constrained minimum variance beamformer etc. and analyzed their limitations  Formulated the Sound source localization algorithms like MUSIC algorithm (Multiple Signal Classification), TDOA and Steered response and currently working on the optimization of it using GAN-LSTM