Listening to wildlife, one soundscape at a time.
Project S.W.A.N. uses passive acoustic monitoring and AI-supported species recognition to reveal bird activity across local landscapes, without disturbing wildlife or habitats.
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Public stations
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Recent activity
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Species references
Sir Archdale Road - Swaffham
House Sparrow
Passer domesticus
Last activity
4m ago
Station
Swaffham
Detections are treated as likely observations. Confidence, background noise and repeated activity all matter when interpreting acoustic wildlife data.
Method
Passive
Stations listen to the surrounding soundscape without attracting or disturbing wildlife.
Analysis
AI-aided
Acoustic events are converted into likely species detections using machine learning.
Output
Records
Each likely detection can include species, station, time and confidence information.
Purpose
Insight
Repeated observations help reveal activity patterns across local habitats.
The science behind S.W.A.N.
Turning sound into ecological observations.
Acoustic monitoring is useful because birds are often heard before they are seen. By listening repeatedly over time, a station can build a picture of which species are active, when they call, and how local soundscapes change.
Passive acoustic monitoring
Stations record the natural soundscape without baiting, playback or direct interaction. This makes the method suitable for repeated observation with minimal disturbance.
AI-supported recognition
Bird calls and songs are analysed against known acoustic patterns. The result is a likely species match, rather than a guaranteed record.
Long-term pattern finding
Repeated detections can help show daily activity, seasonal changes, habitat differences and possible shifts in local biodiversity.
What the network records
Small data points that build a bigger picture.
A single detection is only one clue. Over time, many detections from many stations can help describe how birds use an area.
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Likely species
The bird species most closely matching the detected call or song.
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Time and date
When the detection happened, allowing daily and seasonal patterns to be explored.
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Station context
Which listening station recorded the event, with public locations shown responsibly.
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Confidence
A guide to how strongly the sound matched the suggested species.
Methodology note
Project S.W.A.N. presents detections as likely observations. Acoustic identification can be affected by distance, wind, overlapping calls, background noise and similar-sounding species. Stronger conclusions come from repeated detections, confidence scores and longer-term patterns rather than isolated records.
Explore the live network
A public window into local bird activity.
Each station page turns detection data into clear summaries, recent activity, latest likely species and station-level context.
From sound to science
How a detection becomes useful information.
The aim is not just to identify birds, but to make local soundscape data easier to understand, compare and learn from.
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Listen
A station records the surrounding soundscape from its local habitat.
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Analyse
AI-supported recognition suggests the most likely bird species.
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Interpret
Confidence, repetition and context help decide how useful a detection is.
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Share
Public pages make station activity, maps and bird profiles easier to explore.
Start exploring
See what the network is hearing now.
Open the public map, choose a station and discover the latest likely bird detections across the network.