TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
This work addresses biodiversity monitoring by enabling efficient bird song recognition on low-power devices, though it is incremental as it focuses on empirical comparisons of existing methods.
The paper tackled the challenge of deploying accurate bird species classification models to low-power devices by comparing tinyML neural network architectures and compression techniques, finding that simple architectures can robustly detect individual bird species like corn bunting for deployment on low-power sensors.
Monitoring biodiversity at scale is challenging. Detecting and identifying species in fine grained taxonomies requires highly accurate machine learning (ML) methods. Training such models requires large high quality data sets. And deploying these models to low power devices requires novel compression techniques and model architectures. While species classification methods have profited from novel data sets and advances in ML methods, in particular neural networks, deploying these state of the art models to low power devices remains difficult. Here we present a comprehensive empirical comparison of various tinyML neural network architectures and compression techniques for species classification. We focus on the example of bird song detection, more concretely a data set curated for studying the corn bunting bird species. The data set is released along with all code and experiments of this study. In our experiments we compare predictive performance, memory and time complexity of classical spectrogram based methods and recent approaches operating on raw audio signal. Our results indicate that individual bird species can be robustly detected with relatively simple architectures that can be readily deployed to low power devices.