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The increasing popularity of autonomous and remotely-piloted drones havepaved the way for several use-cases, e.g., merchandise delivery andsurveillance. In many scenarios, estimating with zero-touch the weight of thepayload carried by a drone before its physical approach could be attractive,e.g., to provide an early tampering detection.  In this paper, we investigate the possibility to remotely detect the weightof the payload carried by a commercial drone by analyzing its acousticfingerprint. We characterize the difference in the thrust needed by the droneto carry different payloads, resulting in significant variations of the relatedacoustic fingerprint. We applied the above findings to different use-cases,characterized by different computational capabilities of the detection system.Results are striking: using the Mel-Frequency Cepstral Coefficients (MFCC)components of the audio signal and different Support Vector Machine (SVM)classifiers, we achieved a minimum classification accuracy of 98% in thedetection of the specific payload class carried by the drone, using anacquisition time of 0.25 s---performances improve when using longer timeacquisitions.  All the data used for our analysis have been released as open-source, toenable the community to validate our findings and use such data as aready-to-use basis for further investigations.

Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions