AeroSonicDB (YPAD-0523): Labelled audio dataset for acoustic detection and classification of aircraft

AeroSonicDB (YPAD-0523): Labelled audio dataset for acoustic detection and classification of aircraft Version 1.1 (September 2023) Publication When using this data in an academic work, please reference the DOI and version. Description AeroSonicDB:YPAD-0523 is a specialised dataset of ADS-B labelled audio clips for research in the fields of environmental 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, then human verified and annotated with the first and final moments which the target aircraft is audible. A total of 1,895 audio clips are distributed across two top-level classes, “Aircraft” (8.87 hours) and “Silence” (3.52 hours). The aircraft class is then further broken-down into four subclasses, which broadly describe the structure of the aircraft 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 (10.04 hours) and testing (2.35 hours) subsets, with the training set further split into 5 distinct folds for cross-validation. These splits are performed to prevent data-leakage between folds and the test set, ensuring samples collected in the same recording session (distinct in time, location and microphone) are assigned to the same fold. Researchers may find applications for this dataset in a number of fields; particularly aircraft noise isolation and noise 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 trigger, capture and label audio samples. A 60-second recording is triggered when an aircraft transmits a message indicating it is within a specified distance of the recording device (see “Location data” below for specifics). The resulting audio file is labelled with the unique ICAO identifier code for the aircraft, as well as its last reported altitude, date, time, location and microphone. The recording is then human verified and annotated with timestamps for the first and last moments the aircraft is audible. In total, AeroSonicDB contains 625 recordings of low-altitude aircraft - varying in length from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio. A 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 they are within a specified distance of the recording device (see “Location data” below). These “silence” recordings are also human verified to ensure no aircraft noise is present. The dataset contains 1,270 clips of silence/urban background noise. Location data Recordings have been collected from three (3) locations. GPS coordinates for each location are provided in the "locations.json" file. In order to protect privacy, coordinates have been provided for a road or public space nearby the recording device instead of its exact location. Location: 0 Situated in a suburban environment approximately 15.5km north-east of the start/end of the runway. For Adelaide, typical south-westerly winds bring most arriving aircraft past this location on approach. Winds from the north or east will cause aircraft to take-off to the north-east, however not all departing aircraft will maintain a course to trigger a recording at this location. The "trigger distance" for this location is set for 3km to ensure small/slower aircraft and large/faster aircraft are captured within a sixty-second recording. "Silence" or ambient background noises at this location include; cars, motorbikes, light-trucks, garbage trucks, power-tools, lawn mowers, construction sounds, sirens, people talking, dogs barking and a wide range of Australian native birds (New Holland Honeyeaters, Wattlebirds, Australian Magpies, Australian Ravens, Spotted Doves, Rainbow Lorikeets and others). Location: 1 Situated approximately 500m south-east of the south-eastern end of the runway, this location is nearby recreational areas (golf course, skate park and parklands) with a busy road/highway inbetween the location and runway. This location features heavy winds and road traffic, as well as people talking, walking and riding, and also birds such as the Australian Magpie and Noisy Miner. The trigger distance for this location is set to 1km. Due to their low altitude aircraft are louder, but audible for a shorter time compared to "Location 0". Location: 2 As an alternative to "Location 1", this location is situated approximately 950m south-east of the end of the runway. This location has a wastewater facility to the north, a residential area to the south and a popular beach to the west. This location offers greater wind protection and further distance from airport and highway noises. Ambient background sounds feature close proximity cars and motorbikes, cyclists, people walking, nail guns and other construction sounds, as well as the local birds mentioned above. 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 (81%) and test (19%) sets. The training set has further been split into 5 folds, giving researchers a common split to perform 5-fold cross-validation to ensure reproducibility and comparable 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, its meta information, annotations and airframe features. Alternatively, these labels can be derived directly from the filename of the sample (see below). 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 “Columns/Labels” below for all features). File naming convention Audio samples are in WAV format, with some metadata stored in the filename. 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 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 files) train-test: Train-test split (train, test) fold: Digit from 1 to 5 splitting the training data 5 ways (else test) filename: The filename of the audio recording date: Date of the recording time: Time of the recording location: ID for the location of the recording mic: ID of the microphone used 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) altitude: Approximate altitude of the aircraft (in feet) at the start of the recording hex_id: Unique ICAO 24-bit address for the aircraft recorded session: Unique recording session by time, location and microphone. offset: Time stamp marking the start of the audio event. duration: Length of the recording (in seconds) file_length: Total length of the audio file in seconds. 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 Environmental evaluation audio As a means for evaluating model performance on real-world data, a supplementary set of real-time environmental recordings have been included with AeroSonicDB(YPAD-0523). This additional dataset contains six, one-hour long recordings of continuous urban noise, and is accompanied by a CSV file (environment_class_mappings.csv) annotated with relevant class labels per 5-second interval. Due to the variable length of an aircraft audio event and the lack of distinct onset and outset moments, audio segments which transition between aircraft and silence periods are tagged with an “ignore” class. This is done to provide a clear boundary between silence and aircraft events, helping to avoid false misclassification at event boundaries and ensure meaningful evaluation results. Conditions of use Dataset created by Blake Downward. The AeroSonicDB (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/) Acknowledgements Special thanks to Jon Nordby of Soundsensing AS - his contributions were pivotal in maximising the potential of this dataset for open-source release. Feedback Please send suggestions, feedback and comments to: Blake Downward: [e-mail] Change log 1.1: Replaced truncated aircraft samples with the original full-length files and annotated the beginning and end of each audio event. Added ‘ignore’ statements to aircraft event boundaries in the environmental class mappings file. 1.0: Environmental audio and mappings added 0.3: locations.json file added, README updated 0.2: location information added to READMEAeroSonicDB (YPAD-0523)：用于航空器声学检测与分类的标注音频数据集，版本1.1（2023年9月） 引用说明：若将本数据集用于学术研究，请标注其DOI与版本号。 数据集概述 AeroSonicDB (YPAD-0523) 是专为环境噪声归因与机器听觉（尤其是低空航空器声学检测与分类）研究打造的专用数据集，包含基于广播式自动相关监视（ADS-B, Automatic Dependent Surveillance–Broadcast）标注的音频片段。 本数据集的音频文件录制于阿德莱德国际机场（国际民用航空组织（International Civil Aviation Organization, ICAO）代码：YPAD）05/23号主跑道进离场航线附近区域。录音初始标签基于从头顶航空器接收的无线电（ADS-B）消息生成，随后经人工核验，并标注目标航空器可被闻听的起始与终止时刻。 数据集共包含1895条音频片段，分为两个顶级类别：“航空器”（8.87小时）与“静音”（3.52小时）。其中航空器类别进一步细分为四个子类，大致依据航空器结构与推进机制划分。此外还提供了多种附加“机身”特征，以支持研究人员更精细地操控数据集，并为其特定应用场景构建专属本体。 为方便使用，数据集已划分为训练集（10.04小时）与测试集（2.35小时），其中训练集进一步拆分为5个独立折用于交叉验证。该拆分方式旨在避免折间与测试集的数据泄露，确保同一录制会话（时间、位置与麦克风均不同）的样本被分配至同一折。 本数据集可应用于多个研究领域，尤其包括城市环境中的航空器噪声隔离与监测、辅助雷达技术的被动声学系统开发，以及解析航空器噪声源以助力制造商设计低噪声航空器。 音频数据采集 本数据集利用航空器直接传输的ADS-B消息自动触发、捕获并标注音频样本。当航空器传输表明其距录制设备处于指定距离内的消息时，系统将触发60秒的录音（具体触发距离详见下文“位置数据”章节）。生成的音频文件将标注航空器的唯一ICAO标识符、最后上报的高度、日期、时间、位置与麦克风信息。随后经人工核验，并标注航空器可被闻听的起始与终止时间戳。 总体而言，AeroSonicDB包含625条低空航空器录音，时长介于18至60秒之间，总航空器音频时长为8.87小时。数据集还包含无航空器的城市背景噪声（静音）样本，用于区分特定位置的环境噪声与航空器噪声。“静音”录音仅在无航空器传输处于指定距离内的消息时触发（详见下文“位置数据”章节），且均经人工核验确保无航空器噪声混入。数据集共包含1270条静音/城市背景噪声片段。 位置数据 录音共采集自3个位置。各位置的GPS坐标存储于locations.json文件中，为保护隐私，提供的坐标为录制设备附近的道路或公共空间坐标，而非设备精确位置。 位置0：位于跑道起降端东北约15.5公里的郊区环境。阿德莱德的典型西南风会使多数进近航空器从该位置上方飞过；若刮北风或东风，航空器将向东北方起飞，但并非所有离场航空器都会经过该位置以触发录音。该位置的触发距离设为3公里，以确保在60秒的录音时长内捕获小型/慢速航空器与大型/快速航空器。该位置的“静音”或环境背景噪声包括：汽车、摩托车、轻型卡车、垃圾车、电动工具、割草机、施工声响、警报器、人声、犬吠，以及多种澳大利亚本土鸟类（如新荷兰蜜鸟、垂蜜鸟、澳大利亚喜鹊、澳洲渡鸦、斑鸠、虹彩吸蜜鹦鹉等）。 位置1：位于跑道东南端东南约500米处，邻近休闲区域（高尔夫球场、滑板公园与公园绿地），该位置与跑道之间有繁忙的道路/高速公路。该位置存在强风与道路交通噪声，同时伴随人声、行走与骑行活动，以及澳大利亚喜鹊、黑头矿鸟（Noisy Miner）等鸟类。该位置的触发距离设为1公里。由于航空器处于低空，音量更高，但与位置0相比，可被闻听的时长更短。 位置2：作为位置1的替代方案，该位置位于跑道端东南约950米处。该位置北侧为污水处理设施，南侧为居民区，西侧为热门海滩。该位置的防风性更好，且距机场与高速公路噪声的距离更远。环境背景噪声包括近距离的汽车与摩托车、骑行者、行人、钉枪及其他施工声响，以及前文提及的本土鸟类。 航空器元数据 为拓展本数据集的研究潜力，我们收集了所有航空器的附加“机身”元数据。机身信息从多个开源数据库收集并交叉核验。作者未发现aircraft_meta文件中存在显著错误，但未来版本的数据集计划直接从国际民用航空组织（ICAO）获取航空器信息，以确保采用单一可验证的信息来源。 分类/子类本体（录音时长，单位：分钟） 0. 无航空器（202） 0: 无航空器（202） 1. 航空器（214） 1: 活塞螺旋桨飞机（12） 2: 涡轮螺旋桨飞机（37） 3: 涡轮风扇飞机（163） 4: 旋翼航空器（1.6） 子类结合了“机身”与“发动机类型”特征。由于样本量较少，活塞式与涡轮轴旋翼航空器/直升机被合并为单个子类。 数据拆分 音频录音已划分为训练集（81%）与测试集（19%）。训练集进一步拆分为5折，为研究人员提供通用的拆分方案以开展5折交叉验证，确保研究可复现且结果具有可比性。为避免测试集的数据泄露，通过时间与位置维度确保训练集与测试集的录音互不重叠，即某一位置的测试集样本均晚于该位置的训练集样本录制。 标注数据 整个数据集（训练集与测试集）的标注信息存储于sample_meta.csv文件中。每一行对应一条唯一的录音，包含其元信息、标注与机身特征。此外，也可直接从样本文件名提取标注信息（详见下文）。aircraft_meta.csv与aircraft_meta.json文件可用于查询航空器特定特征，例如制造商、发动机类型、ICAO机型代码等（所有特征详见下文“列/标签”章节）。 文件命名规范 音频样本采用WAV格式，部分元信息存储于文件名中。基本命名规则为：“航空器ID + 日期 + 时间 + 位置ID + 麦克风ID”，格式为XXXXXX_YYYY-MM-DD_hh-mm-ss_X_X。 带航空器的样本示例：{十六进制ID}_{日期}_{时间}_{位置ID}_{麦克风ID}.{文件扩展名}，例如7C7CD0_2023-05-09_12-42-55_2_1.wav。 无航空器的样本：“静音”文件以6个前导零替代航空器十六进制代码。“静音”样本的所有相关元信息均存储于音频文件名中，同时也可在配套的sample_meta.csv文件中查询，格式为000000_{日期}_{时间}_{位置ID}_{麦克风ID}.{文件扩展名}，例如000000_2023-05-09_12-30-55_2_1.wav。 列/标签（存储于sample_meta.csv、aircraft_meta.csv/json文件中） - train-test：训练/测试集划分（train为训练集，test为测试集） - fold：训练集的5折拆分编号（1至5，测试集无此字段） - filename：音频录音的文件名 - date：录音日期 - time：录音时间 - location：录音位置的ID - mic：所用麦克风的ID - class：录音的顶级标签（例如0=无航空器，1=可闻听航空器） - subclass：录音的子类标签（例如0=无航空器，3=涡轮风扇飞机） - altitude：录音开始时航空器的近似高度（单位：英尺） - hex_id：所录制航空器的唯一ICAO 24位地址 - recorded session：基于时间、位置与麦克风唯一标识的录制会话 - offset：音频事件的起始时间戳 - duration：录音时长（单位：秒） - file_length：音频文件的总时长（单位：秒） - reg：航空器的注册号 - airframe：描述航空器的机械结构（例如动力驱动飞机、旋翼航空器） - engtype：发动机类型（例如活塞式、涡轮螺旋桨、涡轮风扇、涡轮轴） - engnum：发动机数量 - shortdesc：描述机身与发动机配置的3位字母数字代码（例如L1P、L4J、H2T） - typedesig：航空器型号的ICAO类型代码（例如PC12、C185、B738） - manu：航空器制造商（例如波音、皮拉图斯、空客） - model：航空器型号（例如737-800、A320-232、DHC-8-315） - engmanu：发动机制造商（例如普惠、CFM国际、罗尔斯·罗伊斯） - engmodel：发动机型号（例如TRENT XWB、CFM56-7B24E、PT6E-67XP） - engfamily：发动机型号系列（例如TRENT、CFM56、PT6） - fueltype：发动机所用燃料类型（例如汽油、航空煤油） - propmanu：螺旋桨制造商（例如Hartzell螺旋桨、汉密尔顿标准、“未配备螺旋桨的航空器”） - propmodel：螺旋桨型号（例如HC-E5A-3A/NC10245B、14SF-15、“不适用”） - mtow：最大起飞重量（MTOW，单位：千克） 环境评估音频 为支持研究人员在真实场景下评估模型性能，本数据集附带一组补充的实时环境录音。该附加数据集包含6段时长1小时的连续城市噪声录音，配套有environment_class_mappings.csv文件，该文件按5秒间隔标注了相关类别标签。由于航空器音频事件的时长可变且缺乏明确的起止时刻，在航空器与静音时段之间过渡的音频片段将被标记为“忽略”类别。此举旨在明确静音与航空器事件的边界，避免在事件边界处出现错误分类，确保评估结果具有实际意义。 使用条款 本数据集由Blake Downward创建。AeroSonicDB (YPAD-0523) 数据集可在知识共享署名-非商业性使用4.0国际许可（CC BY-NC 4.0）下免费用于非商业用途。[https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) 致谢 特别感谢Soundsensing AS的Jon Nordby，其贡献对于本数据集开源发布的潜力最大化起到了关键作用。 反馈 如有建议、反馈或评论，请联系：Blake Downward: [e-mail] 更新日志 1.1：将截断的航空器样本替换为原始完整长度文件，并标注每个音频事件的起始与终止时刻。在环境类别映射文件中为航空器事件边界添加“忽略”标记。 1.0：添加环境音频与映射文件 0.3：添加locations.json文件，更新README 0.2：在README中添加位置信息