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DDL: DATASET AND BASELINE METHODS FOR DRONE DETECTION AND LOCALIZATION USING SOUND

Authors: Saeid Safavi; Safavi, Mohammad Amin; Cook, Ben; Wenwu Wang;

DDL: DATASET AND BASELINE METHODS FOR DRONE DETECTION AND LOCALIZATION USING SOUND

Abstract

This dataset contains drone audio sound recorded in the field and also an artificially synthesized version which is rendered by the sound particle software. The audio files are placed in separate folders and the naming convention is described below: For field recording the file name format is as follows: Filename Indices: 0-12: UTC Timestamp - assumes UNIX epoch (i.e. "2022/01/01 00:00:00.000") 13-16: Sample class - indicating drone model (MINI for DJI Mini2, PRO4 for DJI Phantom Pro 4, and XXXX for no drone.) 17-19: Bearing to Target - ranged from 0-359 degrees with 1 deg increments. 20-22: Range to Target - Distance between microphone array and drone, 1m increments 23-25: Target Altitude - Altitude of the drone AGL, 1m increments 26-29: Ambient Temperature - In Kelvin degrees with 0.1 degrees increments 30: Sample type - Reserved symbols, "R" for real sample and "S" for synthesized sample 31-36: Flight Session ID - metadata 37-42: Recording session ID - metadata 43-48: Sample Sequence ID, this can be used to merge samples within a recording session (if you would like to have 200ms or larger audio samples) For artificially synthesized data the format is:

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Keywords

dataset, DOA, drone audio

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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