Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2311.06368

The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft

Blake Downward·Jon Nordby
View original source ↗Read source document ↗Export metadata ↓

Abstract

The time and expense required to collect and label audio data has been a prohibitive factor in the availability of domain specific audio datasets. As the predictive specificity of a classifier depends on the specificity of the labels it is trained on, it follows that finely-labelled datasets are crucial for advances in machine learning. Aiming to stimulate progress in the field of machine listening, this paper introduces AeroSonicDB (YPAD-0523), a dataset of low-flying aircraft sounds for training acoustic detection and classification systems. This paper describes the method of exploiting ADS-B radio transmissions to passively collect and label audio samples. Provides a summary of the collated dataset. Presents baseline results from three binary classification models, then discusses the limitations of the current dataset and its future potential. The dataset contains 625 aircraft recordings ranging in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio. These 625 samples feature 301 unique aircraft, each of which are supplied with 14 supplementary (non-acoustic) labels to describe the aircraft. The dataset also contains 3.52 hours of ambient background audio ("silence"), as a means to distinguish aircraft noise from other local environmental noises. Additionally, 6 hours of urban soundscape recordings (with aircraft annotations) are included as an ancillary method for evaluating model performance, and to provide a testing ground for real-time applications.

Explore related subjects

cs.SDcs.LGeess.AS

Keep this discovery

BibTeXRIS

Blake Downward, Jon Nordby. 2023-11-10. The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft. https://arxiv.org/abs/2311.06368

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD↗
arXiv2026

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD↗