DroneAudioset: An Audio Dataset for Drone-based Search and Rescue
Abstract
Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The dataset is publicly available at https://huggingface.co/datasets/ahlab-drone-project/DroneAudioSet/ under the MIT license), a comprehensive drone audition dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This dataset is an important step towards enabling design and deployment of drone-audition systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents DroneAudioset, a comprehensive audio dataset tailored for drone-based search and rescue, addressing critical gaps in existing datasets. The technical contribution is significant, as it lays the groundwork for advancing audio processing techniques in challenging environments, although further experimental validation is needed to fully realize its potential impact.
Methodology
The methodology is centered around the creation of a novel audio dataset specifically designed for drone-based search and rescue operations. The authors have taken into account various factors such as signal-to-noise ratios, drone types, and environmental conditions, which are crucial for real-world applications. However, while the dataset is comprehensive, the paper could benefit from a more detailed explanation of the data collection process and the specific algorithms used for annotation and classification.
Results & evaluation
The evaluation section discusses the potential applications of the dataset in developing noise suppression and classification methods, but it lacks concrete experimental results demonstrating the effectiveness of these methods. Including preliminary results or benchmarks would strengthen the paper significantly.
Reproducibility
The dataset is publicly available, which is a positive aspect for reproducibility. However, the paper does not provide enough detail regarding the experimental setup or the algorithms used for processing the audio data, which may hinder full reproducibility of results.
Limitations
One limitation is the potential bias in the dataset due to the specific environments and conditions under which the audio was recorded. Additionally, the dataset may not cover all possible scenarios encountered in search and rescue missions, limiting its generalizability.
Broader impact
The development of the DroneAudioset has significant implications for improving drone-based search and rescue operations, particularly in challenging conditions where visual methods fail. This could enhance emergency response efforts and save lives by enabling more effective detection of human presence. The paper presents DroneAudioset, a comprehensive audio dataset tailored for drone-based search and rescue, addressing critical gaps in existing datasets. The technical contribution is significant, as it lays the groundwork for advancing audio processing techniques in challenging environments, although further experimental validation is needed to fully realize its potential impact.
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