The Dawn of Bird Shazam
By Punnag Choudhury, Aayush Gupta, Prisha Gupta & Sehajpreet Kaur
In the quiet hills of Nagaland, the forest begins to stir as dawn breaks. The calls of hornbills echo across misty valleys, and the melodic warble of songbirds weaves through the trees like a living symphony. These sounds have always been nature’s way of speaking; a language of presence, movement, and survival. And for the first time in history, we may be learning to listen with the help of artificial intelligence.
Just as Shazam helps people identify a song playing in a café, researchers are now building systems that can recognize birds by their calls. The idea of a “Bird Shazam” may sound whimsical, but it has very real implications for how we study and protect biodiversity. In regions like Northeast India, which are rich in birdlife yet difficult to monitor, such technology could transform how conservation is done on the ground.
Traditionally, identifying birds by their songs was the job of experts: ornithologists, birdwatchers, or indigenous communities who spent years learning to distinguish subtle variations in pitch and rhythm. In Nagaland and other parts of the Northeast, villagers have long recognized birds not only by sight but also by voice. This skill has been passed down through generations, woven into folklore, daily routines, and even local farming practices.
However, as climate change accelerates and forests shrink, traditional methods of observation are no longer enough. Bird populations are shifting rapidly, and scientists need scalable ways to track these changes across time and space. That’s where AI steps in, not to replace human knowledge, but to enhance it.
At the core of this technology is a deceptively simple element: sound. When a bird sings, its call can be recorded using a phone, a field recorder, or an automated sensor. That audio is then turned into a spectrogram; a visual map of sound that shows how the pitch and frequency of the call change over time. To the human eye, a spectrogram might look like abstract art. But to a trained AI model, it holds clues to the bird’s identity.
Two types of machine learning tools are commonly used in bird call recognition. First are convolutional neural networks (CNNs), which are designed to detect patterns in visual data. CNNs analyze the shapes, curves, and textures of the spectrogram, just as they might identify objects in a photograph. Second are transformer models, which were originally developed to understand human language. These help the system interpret how bird calls evolve over time, giving it a sense of rhythm and sequence.
The result is a model that can “listen” to a recording and tell you, with surprising accuracy, which species are present. But this isn’t magic. It requires massive amounts of data to work well. Databases like Xeno-Canto and Cornell’s Macaulay Library offer millions of recordings from around the world.
These recordings are used to train the models, often with simulated variations; like background noise or altered pitch to help the system learn in real-world conditions.
Once trained, these systems are tested on new recordings. Many achieve accuracy rates of 70 to 80 percent, even across hundreds of species. For researchers, this opens up exciting possibilities.
Devices can now be deployed in remote forests, running 24/7, capturing soundscapes with no human presence required. In places like the highlands of Nagaland or the floodplains of Assam, where field surveys are logistically challenging, this is a major breakthrough.
But no technology is perfect. One major issue is the “cocktail party problem”; a term borrowed from audio engineering. In nature, birds rarely call one at a time. Instead, dozens of species may be vocalizing at once, creating a layered, complex soundscape. Untangling these overlapping calls is still a difficult task for AI.
There’s also the issue of bias. Most AI models perform better on species that are common or well- documented. Rare, nocturnal, or endangered birds often have too few recordings to train a model effectively. Ironically, the birds we most need to monitor are often the hardest to detect. And then there’s noise—wind, rain, insects, and distant traffic can all interfere with a clean recording.
Another challenge is context. A human expert might know that a certain bird is only active at dusk or only found in specific habitats. AI, at least for now, doesn’t have this ecological intuition. It might mistakenly identify a species in an area where it’s unlikely to be found, simply because the sound is a close match.
That’s why many researchers believe the future lies in combining machine intelligence with local wisdom. In Northeast India, communities already have deep ecological knowledge. If villagers could contribute recordings via mobile apps or simple recorders, a locally trained AI system could emerge, one that recognizes regional species with greater precision and respects cultural nuances in naming and recognition.
This collaboration could also build community ownership over conservation data. Instead of relying solely on outside scientists, residents could become active stewards of biodiversity, tracking seasonal changes, migration patterns, and even the impact of development projects. Schools could use bird call recordings as teaching tools. Farmers could track birds linked to crop cycles. And local governments could use the data to make more informed land-use decisions.
Looking ahead, scientists are working on systems that combine sound with other types of information like GPS, weather, time of day, and even video footage. These “multimodal” models may offer a more complete picture of bird behavior. There’s also growing interest in self-supervised learning, a technique where AI learns patterns without needing labeled examples. This could help solve the rare species problem by allowing the system to detect new birds after hearing them only a few times. Still, it’s important to keep the big picture in mind. The point of this technology isn’t just to identify a bird, it’s to understand what that bird is telling us. Are its numbers declining? Is its habitat shrinking? Are migratory patterns shifting due to rising temperatures? AI can help answer these questions faster and more accurately, giving conservationists a fighting chance in the race to protect biodiversity.
In a place like Northeast India, where ecological richness meets cultural depth—this work takes on even greater urgency. The region is home to over 800 bird species, many found nowhere else. But it’s also under threat from mining, deforestation, and infrastructure development. Tools like Bird Shazam won’t solve these problems alone, but they can shine a light on what’s being lost—and why it matters. Each morning, as Nagaland’s forests fill with birdsong, a quiet revolution is taking place. These melodies are no longer just background noise. They’re data. They’re warnings. They’re stories. And with the help of AI, we are finally learning to listen—before it’s too late.
(The authors: Punnag Choudhury, Aayush Gupta, Prisha Gupta, and Sehajpreet Kaur are 3rd B.Tech students at Plaksha University, specializing in Data Science, Economics, and Business. United by a shared passion for solving complex real-world problems, they blend analytical depth with creative thinking across technology, strategy, and innovation. Their collaborative spirit and diverse strengths have powered projects that push boundaries and deliver tangible impact. Together, they represent the next generation of change-makers at the intersection of data, business, and design).