  Owing to small size, sensing capabilities and autonomous nature, the Unmanned
Air Vehicles (UAVs) have enormous applications in various areas, e.g., remote
sensing, navigation, archaeology, journalism, environmental science, and
agriculture. However, the unmonitored deployment of UAVs called the amateur
drones (AmDr) can lead to serious security threats and risk to human life and
infrastructure. Therefore, timely detection of the AmDr is essential for the
protection and security of sensitive organizations, human life and other vital
infrastructure. AmDrs can be detected using different techniques based on
sound, video, thermal, and radio frequencies. However, the performance of these
techniques is limited in sever atmospheric conditions. In this paper, we
propose an efficient unsupervise machine learning approach of independent
component analysis (ICA) to detect various acoustic signals i.e., sounds of
bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario.
After unmixing the signals, the features like Mel Frequency Cepstral
Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square
Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD
signals are extracted by first passing the signals from octave band filter
banks. Based on the above features the signals are classified using Support
Vector Machines (SVM) and K Nearest Neighbor (KNN) to detect the presence or
absence of AmDr. Unique feature of the proposed technique is the detection of a
single or multiple AmDrs at a time in the presence of multiple acoustic
interfering signals. The proposed technique is verified through extensive
simulations and it is observed that the RMS values of PSD with KNN performs
better than the MFCC with KNN and SVM.
