# HumBugDB: A Large-scale Acoustic Mosquito Dataset arXiv: https://arxiv.org/abs/2110.07607 ยท HTML: https://mlsanity.com/paper/2110.07607/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 58 references that are in the map. - First posted on arXiv 14 Oct 2021. ## Builds on (most cited first, 16 of 16) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016) - [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018) - [Rethinking CNN Models for Audio Classification](https://mlsanity.com/paper/2007.11154/) (2020) - [End-to-end learning for music audio tagging at scale](https://mlsanity.com/paper/1711.02520/) (2017) - [Loss-Calibrated Approximate Inference in Bayesian Neural Networks](https://mlsanity.com/paper/1805.03901/) (2018) - [Flying Insect Classification with Inexpensive Sensors](https://mlsanity.com/paper/1403.2654/) (2014) - [Post-hoc loss-calibration for Bayesian neural networks](https://mlsanity.com/paper/2106.06997/) (2021) - [Sound Event Localization and Detection Using Activity-Coupled Cartesian DOA Vector and RD3net](https://mlsanity.com/paper/2006.12014/) (2020) - [Mosquito Detection with Neural Networks: The Buzz of Deep Learning](https://mlsanity.com/paper/1705.05180/) (2017) - [Mosquito detection with low-cost smartphones: data acquisition for malaria research](https://mlsanity.com/paper/1711.06346/) (2017) - [Cost-sensitive detection with variational autoencoders for environmental acoustic sensing](https://mlsanity.com/paper/1712.02488/) (2017) - [Super-resolution of Time-series Labels for Bootstrapped Event Detection](https://mlsanity.com/paper/1906.00254/) (2019) - [HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset](https://mlsanity.com/paper/2001.04733/) (2020) ## Built on by (most cited first, 15 of 15) - [Transferable Models for Bioacoustics with Human Language Supervision](https://mlsanity.com/paper/2308.04978/) (2023) - [Data Representativity for Machine Learning and AI Systems](https://mlsanity.com/paper/2203.04706/) (2022) - [Few-shot bioacoustic event detection at the DCASE 2022 challenge](https://mlsanity.com/paper/2207.07911/) (2022) - [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022) - [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025) - [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023) - [Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection](https://mlsanity.com/paper/2305.16691/) (2023) - [The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes](https://mlsanity.com/paper/2205.06799/) (2022) - [ISPA: Inter-Species Phonetic Alphabet for Transcribing Animal Sounds](https://mlsanity.com/paper/2402.03269/) (2024) - [Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds](https://mlsanity.com/paper/2509.04166/) (2025) - [Finding Needles in the Haystack: Transductive Active Labeling in Ecology](https://mlsanity.com/paper/2606.03821/) (2026) - [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026) - [Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations](https://mlsanity.com/paper/2207.13843/) (2022) - [An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2509.11168/) (2025) - [State Space Models for Bioacoustics: A Comparative Evaluation with Transformers](https://mlsanity.com/paper/2512.03563/) (2025) ## Abstract (arXiv) This paper presents the first large-scale multi-species dataset of acoustic recordings of mosquitoes tracked continuously in free flight. We present 20 hours of audio recordings that we have expertly labelled and tagged precisely in time. Significantly, 18 hours of recordings contain annotations from 36 different species. Mosquitoes are well-known carriers of diseases such as malaria, dengue and yellow fever. Collecting this dataset is motivated by the need to assist applications which utilise mosquito acoustics to conduct surveys to help predict outbreaks and inform intervention policy. The task of detecting mosquitoes from the sound of their wingbeats is challenging due to the difficulty in collecting recordings from realistic scenarios. To address this, as part of the HumBug project, we conducted global experiments to record mosquitoes ranging from those bred in culture cages to mosquitoes captured in the wild. Consequently, the audio recordings vary in signal-to-noise ratio and contain a broad range of indoor and outdoor background environments from Tanzania, Thailand, Kenya, the USA and the UK. In this paper we describe in detail how we collected, labelled and curated the data. The data is provided from a PostgreSQL database, which contains important metadata such as the capture method, age, feeding status and gender of the mosquitoes. Additionally, we provide code to extract features and train Bayesian convolutional neural networks for two key tasks: the identification of mosquitoes from their corresponding background environments, and the classification of detected mosquitoes into species. Our extensive dataset is both challenging to machine learning researchers focusing on acoustic identification, and critical to entomologists, geo-spatial modellers and other domain experts to understand mosquito behaviour, model their distribution, and manage the threat they pose to humans.