AeroSonic YPAD-0523: Labelled audio dataset for acoustic detection and classification of aircraft

AeroSonic YPAD-0523: Labelled audio dataset for acoustic detection and classification of aircraft Version 0.1 (June 2023) Publication If using this data in an academic work, please reference the DOI and version. Description AeroSonic:YPAD-0523 is a specialised dataset of ADS-B labelled audio clips for research in the fields of aircraft noise attribution and machine listening, particularly acoustic detection and classification of low-flying aircraft. Audio files in this dataset were recorded at locations in close proximity to a flight path approaching or departing Adelaide International Airport’s (ICAO code: YPAD) primary runway, 05/23. Recordings are initially labelled from radio (ADS-B) messages received from the aircraft overhead. Each recording is then human verified, and trimmed to the best (subjective) 20 seconds of audio in which the target aircraft is audible. A total of 1,890 audio clips are balanced across two top-level classes, “Aircraft” (3.57 hours: 642 20-second recordings) and “Silence” (3.37 hours: 1,248 5 and 10-second recordings). The aircraft class is then further broken-down into four unbalanced subclasses which broadly describe an aircrafts structure and propulsion mechanism. A variety of additional "airframe" features are provided to give researchers finer control of the dataset, and the opportunity to develop ontologies specific to their own use case. For convenience, the dataset has been split into training (6.28 hours) and testing (0.66 hours) subsets, with the training set further split into 10 folds for cross-validation. Care has been taken to ensure the class distribution for each subset and fold does not significantly deviate from the overall distribution. Researchers may find applications for this dataset in a number of fields; particularly aircraft noise isolation and monitoring in an urban environment, development of passive acoustic systems to assist radar technology, and understanding the sources of aircraft noise to help manufacturers design less-noisy aircraft. Audio data ADS-B (Automatic Dependent Surveillance–Broadcast) messages transmitted directly from aircraft are used to automatically capture and label audio recordings. A 60-second recording is triggered when an aircraft transmits a message indicating it is within a specified distance of the recording device. The file is labelled with a unique ICAO identifier code for the aircraft, as well as its last recorded altitude, date and time. The recording is then human verified and trimmed to 20 seconds - with the aircraft audible for the duration of the clip. A balanced collection of urban background noise without aircraft (silence) is included with the dataset as a means of distinguishing location specific environmental noises from aircraft noises. 10-second background noise, or “silence” recordings are triggered only when there are no aircraft broadcasting that they are within a specified distance of the recording device. These "silence" recordings are also human verified to ensure no aircraft noise is present. The dataset contains 1,180 10-second clips, and 68 5-second clips of silence/background noise. Aircraft metadata Supplementary "airframe" metadata for all aircraft has been gathered to help broaden the research possibilities from this dataset. Airframe information was collected and cross-checked from a number of open-source databases. The author has no reason to beleive any significant errors exist in the "aircraft_meta" files, however future versions of this dataset plan to obtain aircraft information directly from ICAO (International Civil Aviation Organization) to ensure a single, verifiable source of information. Class/subclass ontology (minutes of recordings) 0. no aircraft (202) 0: no aircraft (202) 1. aircraft (214) 1: piston-propeller aeroplane (12) 2: turbine-propeller aeroplane (37) 3: turbine-fan aeroplane (163) 4: rotorcraft (1.6) The subclasses are a combination of the "airframe" and "engtype" features. Piston and Turboshaft rotorcraft/helicopters have been combined into a single subclass due to the small number of samples. Data splits Audio recordings have been split into training (90.5%) and test (9.5%) sets. The training set has further been split into 10 folds, giving researchers a common split to perform 10-fold cross-validation - ensuring reproducibility and comparative results. Data leakage into the test set has been avoided by ensuring recordings are disjointed from the training set by time and location - meaning samples in the test set for a particular location were recorded after any samples included in the training set for that particular location. Labelled data The entire dataset (training and test) is referenced and labelled in the "sample_meta.csv" file. Each row contains a reference to a unique recording and all the labels and features associated with that recording and aircraft. Alternatively, these labels can be derived directly from the filename of the sample (see below), plus a JSON file which accompanies each aircraft sample. The "aircraft_meta.csv" and "aircraft_meta.json" files can be used to reference aircraft specific features - such as; manufacturer, engine type, ICAO type designator etc. (see below for all 14 airframe features). File naming convention Audio samples are in *WAV* format, and metadata for aircraft recordings are stored as *JSON* files. Both files share the same name, only differing by their file extension. Basic Convention “Aircraft ID + Date + Time + Location ID + Microphone ID” “XXXXXX_YYYY-MM-DD_hh-mm-ss_X_X” Sample with aircraft {hex_id} _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext} * 7C7CD0_2023-05-09_12-42-55_2_1.wav * 7C7CD0_2023-05-09_12-42-55_2_1.json Sample without aircraft “Silence” files are denoted with six (6) leading zeros rather than an aircraft hex code. All relevant metadata for “silence” samples are contained in the audio filename, and again in the accompanying “sample_meta.csv” 000000 _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext} 000000_2023-05-09_12-30-55_2_1.wav Columns/Labels (found in sample_meta.csv, aircraft_meta.csv/json and aircraft recording JSON files) train-test: Train-test split (*train*, *test*) fold: Digit from 0 to 9 splitting the training subset 10 ways (else *test*) filename: The filename of the audio recording date: Date of the recording time: Time of the recording duration: Length of the recording (in seconds) location_id: ID for the location of the recording microphone_id: ID of the microphone used hex_id: Unique ICAO 24-bit address for the aircraft recorded altitude: Approximate altitude of the aircraft (in feet) at the start of the recording class: Top-level label for the recording (eg. 0 = No aircraft, 1 = Aircraft audible) subclass: Subclass label for the recording (eg. 0 = No aircraft, 3 = Turbine-fan aeroplane) reg: Registration number of the aircraft airframe: Describes the mechanical structure of the aircraft (eg. Power Driven Aeroplane, Rotorcraft) engtype: Type of engine (eg. Piston, Turboprop, Turbofan, Turboshaft) engnum: Number of engines shortdesc: 3 character alpha-numeric code describing the airframe and engine configuration (eg. L1P, L4J, H2T) typedesig: ICAO type designator for make and model of aircraft (eg. PC12, C185, B738) manu: Aircraft manufaturer (eg. Boeing, Pilatus, Airbus) model: Aircraft model (eg. 737-800, A320-232, DHC-8-315) engmanu: Engine manufacturer (eg. Pratt & Whitney, CFM Interntional, Rolls Royce) engmodel: Engine model (eg. TRENT XWB, CFM56-7B24E, PT6E-67XP) engfamily: Family of the engine model (eg. TRENT, CFM56, PT6) fueltype: Fuel type used in the engine (eg. Gasoline, Kerosine) propmanu: Propeller manufacturer (eg. Hartzell Propellers, Hamilton Standard, "Aircraft Not Fitted With Propeller") propmodel: Propeller model (eg. HC-E5A-3A\/NC10245B, 14SF-15, "Not Applicable" mtow: Maximum take off weight (MTOW) in kilograms Conditions of use Dataset created by Blake Downward. The AeroSonic YPAD-0523 dataset is offered free of charge for non-commercial use under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) Feedback Please send suggestions, feedback and comments to: Blake Downward: [e-mail]