Many people enjoy identifying the birds they see and hear outside. However, it can be challenging to identify birds by sight or sound, especially for beginners. This has led to an interest in developing technologies that can automatically identify birds from visual or audio input, essentially creating a “Shazam for birds”.
Shazam is an application that can identify music, movies, and TV shows based on a short audio sample. It matches the acoustic fingerprint of the sample against a database to find a match. Could similar technology be used to instantly identify bird species? Let’s explore some of the options and challenges.
Visual identification
Identifying birds visually requires analyzing their physical features like plumage pattern and color, beak shape, body proportions, etc. This is relatively easy for humans looking at high quality photographs in field guides or birding apps. However, it’s much more difficult to reliably identify birds from brief sightings in the wild. The variations within a species and similarities between some species make visual identification tricky.
Automated visual recognition faces additional challenges. Factors like the bird’s posture, distance, lighting conditions, and obstructions can affect computer vision. Progress is being made with deep learning algorithms that can analyze characteristics and patterns from thousands of bird photos to identify species. Some apps can now identify common backyard birds with reasonable accuracy. However, performance declines for less common species and under suboptimal viewing conditions. More training data and algorithm refinement is needed to match human visual identification skills.
Audio identification
Identifying birds by sound avoids some of the difficulties of field visual recognition. Each bird species has distinctive vocalizations that experienced birders learn to recognize. Automated audio identification aims to replicate this for the average person.
The simplest approach is a fingerprinting system like Shazam. An algorithm analyzes the spectrogram of the bird vocalization to create a unique acoustic fingerprint. It compares this against a database of fingerprints from known bird recordings to find a match and identify the species.
Some bird ID apps like BirdNET use this method. They can recognize common vocalizations of over 1000 species in regions like North America and Europe with reasonable accuracy. Performance is best for clear solo recordings made close to the bird. Results worsen for distant recordings with overlapping sounds. The fingerprint database needs continuous expansion to keep up with discovering new vocalizations.
More advanced systems use machine learning classifiers instead of fingerprints. These are trained on audio recordings of known bird species to learn the distinguishing features of each vocalization. Classification algorithms like support vector machines, random forests, and neural networks can be used. They can match sounds to species even if the exact vocalization wasn’t in the training data.
For example, Song Sleuth uses neural networks trained on over 200,000 recordings covering most North American land birds. It tries to identify key components of the vocalization to classify the bird species. Such systems will improve as more training data becomes available. Combining audio identification with information on location, habitat, season, etc. can also increase accuracy.
Challenges
Automatically identifying bird species by sight or sound has some inherent challenges:
– Large number of bird species – There are around 10,000 bird species worldwide with diverse visual and audio characteristics. Gathering sufficient labeled training data to represent all species is difficult. Recognition systems are limited by their database scope.
– Variability within species – Plumage, size, vocalizations etc. can vary significantly between subspecies, males and females, and individual birds of the same species. Even experts can struggle with some intra-species variations.
– Inter-species similarities – Some species share very similar physical and vocal traits. Automated systems can struggle to reliably distinguish them. Geographical variations further complicate things.
– Environmental conditions – Visual identification is affected by lighting, obstructions, distance and bird posture. Audio quality is impacted by background noise and distortion. Algorithms must be robust to these variables.
– New and rare species – Automated systems depend heavily on training data from known species. Identifying unknown or rare birds outside the training distribution is difficult. The system database must be updated continuously.
– Field usage challenges – Factors like limited computational power, internet connectivity, microphones, etc. on mobile devices constrain real-time automated identification in the field. Performance may not match lab conditions.
The future
Automated bird identification tools are still in their infancy but rapid advancements are being made driven by advances in computer vision and audio recognition. At the same time, citizen scientist projects and audio recorders continue to provide training data to improve the algorithms.
With sufficient data and computing power, automated systems could one day rival or even exceed humans in identifying birds from sight and sound. But a universal Shazam-like system that can identify any bird species in any environment remains challenging.
Specialized regional systems are more feasible in the near term. These could allow casual birdwatchers to identify frequent local species reasonably accurately under favorable conditions. Serious birders will likely continue using a mix of technology and their own expertise.
Integrating multiple data sources could be the future. Systems that combine audio recordings, photos, location, and behavioral clues modeled on expert birders could achieve higher accuracy. Internet-connected sensor networks may also help crowdsource bird monitoring.
For the foreseeable future, automated tools will complement rather than fully replace traditional bird identification. However, they have already made birdwatching more accessible. With continued growth in computing power and training data, these tools will keep improving and unlocking the hobby for more people around the world. The future of birding indeed looks promising.
Conclusion
Automated bird identification has seen great progress but is still facing challenges in matching human-level recognition abilities. Audio recognition systems using machine learning currently perform better than vision-based approaches. However, there are efforts to develop apps that can rival experts in identifying birds in the wild by sight or sound.
While a perfect “Shazam for birds” remains aspirational, specialized regional identification tools are viable in the near future. These could allow casual birdwatchers to identify frequent local species under suitable conditions. Serious birders will likely continue using both technology and personal expertise.
Advancements in sensors, connectivity, and collaborative data could enable next-generation automated identification. But a birder’s skills will continue to be invaluable. Birding technology will complement human abilities for the benefit of science and hobbyists alike. The future will see humans and machines collaborating to watch birds better, not compete to replace each other.