Monitoring small, mobile organisms is crucial for science and conservation, but is technically challenging. Migratory birds are prime examples, often undertaking nocturnal movements of thousands of kilometres over inaccessible and inhospitable geography. Acoustic technology could facilitate widespread monitoring of nocturnal bird migration with minimal human effort. Acoustics complements existing monitoring methods by providing information about individual behaviour and species identities, something generally not possible with tools such as radar. However, the need for expert humans to review audio and identify vocalizations is a challenge to application and development of acoustic technologies. Here, we describe an automated acoustic monitoring pipeline that combines acoustic sensors with machine listening software (BirdVoxDetect). We monitor 4 months of autumn migration in the northeastern United States with five acoustic sensors, extracting nightly estimates of nocturnal calling activity of 14 migratory species with distinctive flight calls. We examine the ability of acoustics to inform two important facets of bird migration: (1) the quantity of migrating birds aloft and (2) the migration timing of individual species. We validate these data with contemporaneous observations from Doppler radars and a large community of citizen scientists, from which we derive independent measures of migration passage and timing. Together, acoustic and weather data produced accurate estimates of the number of actively migrating birds detected with radar. A model combining acoustic data, weather and seasonal timing explained 75% of variation in radar‐derived migration intensity. This model outperformed models that lacked acoustic data. Including acoustics in the model decreased prediction error by 33%. A model with only acoustic information outperformed a model comprising weather and date (57% vs. 48% variation explained, respectively). Acoustics also successfully measured migration phenology: species‐specific timing estimated by acoustic sensors explained 71% of variation in timing derived from citizen science observations. Synthesis and applications. Our results demonstrate that cost‐effective acoustic sensors can monitor bird migration at species resolution at the landscape scale and should be an integral part of management toolkits. Acoustic monitoring presents distinct advantages over radar and human observation, especially in inaccessible and inhospitable locations, and requires significantly less expense. Managers should consider using acoustic tools for monitoring avian movements and identifying and understanding dangerous situations for birds. These recommendations apply to a variety of conservation and policy applications, including mitigating the impacts of light pollution, siting energy infrastructure (e.g. wind turbines) and reducing collisions with structures and aircraft.

The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networks combine low latency with a high taxonomic specificity. However, the scarcity of flight calls in bioacoustic monitoring scenes (below 0.1% of total recording time) requires the automation of audio content analysis. In this article, we address the problem of scaling up the detection and classification of flight calls to a full-season dataset: 6672 hours across nine sensors, yielding around 480 million neural network predictions. Our proposed pipeline, BirdVox, combines multiple machine learning modules to produce per-species flight call counts. We evaluate BirdVox on an annotated subset of the full season (296 hours) and discuss the main sources of estimation error which are inherent to a real-world deployment: mechanical sensor failures, sensitivity to background noise, misdetection, and taxonomic confusion.  After developing dedicated solutions to mitigate these sources of error, we demonstrate the usability of BirdVox by reporting a species-specific temporal estimate of flight call activity for the Swainson's Thrush (Catharus ustulatus).

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While the estimation of what sound sources are, when they occur, and from where they originate has been well-studied, the estimation of how loud these sound sources are has been often overlooked. Current solutions to this task, which we refer to as source-specific sound level estimation (SSSLE), suffer from challenges due to the impracticality of acquiring realistic data and a lack of robustness to realistic recording conditions. Recently proposed weakly supervised source separation offer a means of leveraging clip-level source annotations to train source separation models, which we augment with modified loss functions to bridge the gap between source separation and SSSLE and to address the presence of background. We show that our approach improves SSSLE performance compared to baseline source separation models and provide an ablation analysis to explore our method's design choices, showing that SSSLE in practical recording and annotation scenarios is possible.