Acoustic detection and tracking of UAVs is considered by means of Unattended Ground Sensors equipped with microphonic sensors. Experimental campaigns were conducted with flying drones (DJI, Parrot…) in an anechoic chamber and in countryside. The acoustic database includes various scenario such as hovering flight, translation flight, etc. At the same time, "disturbing noises" have been recorded: ambient noises including birds, insects, people speaking, detonations and fire shot noises have been recorded to feed our database. A part of the recorded database has been used to train a classifier (learning phase). Then another part of the dataset was used to estimate the F-score to evaluate both the precision and recall of the classifier. Adding artificial noise to the data, and selecting acoustic features with evolutionary programming enabled the detection of an unknown drone in an unknown soundscape within 200 meters with the JRip classifier (Fscore of 0.88 for distances between 0 and 100 m, and 0.56 between 100 and 200 m). Main results obtained during the signature analysis and the classifier assessment will be presented and the perspectives in terms of performance improvement with the use of MEMS multi-microphones array.