# TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors

arXiv: https://arxiv.org/abs/2407.21453 · HTML: https://mlsanity.com/paper/2407.21453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 20 of 20)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [tinyBenchmarks: evaluating LLMs with fewer examples](https://mlsanity.com/paper/2402.14992/) (2024)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge](https://mlsanity.com/paper/1807.05812/) (2018)
- [Streaming convolutional neural networks for end-to-end learning with multi-megapixel images](https://mlsanity.com/paper/1911.04432/) (2019)
- [BEANS: The Benchmark of Animal Sounds](https://mlsanity.com/paper/2210.12300/) (2022)
- [End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network](https://mlsanity.com/paper/1904.08990/) (2019)
- [MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory](https://mlsanity.com/paper/2310.16898/) (2023)
- [How to Use Dropout Correctly on Residual Networks with Batch Normalization](https://mlsanity.com/paper/2302.06112/) (2023)
- [A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device](https://mlsanity.com/paper/2211.13005/) (2022)
- [A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers](https://mlsanity.com/paper/2402.10748/) (2024)
- [U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT](https://mlsanity.com/paper/2306.14574/) (2023)
- [Tiny Transformers for Environmental Sound Classification at the Edge](https://mlsanity.com/paper/2103.12157/) (2021)
- [Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers](https://mlsanity.com/paper/2208.02765/) (2022)
- [SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation](https://mlsanity.com/paper/1812.00149/) (2018)
- [Multispecies bird sound recognition using a fully convolutional neural network](https://mlsanity.com/paper/2402.05489/) (2024)

## Built on by (most cited first, 4 of 4)

- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [Future of Edge AI in biodiversity monitoring](https://mlsanity.com/paper/2602.13496/) (2026)
- [TinyD'ej`aVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streams](https://mlsanity.com/paper/2512.09786/) (2025)
- [PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors](https://mlsanity.com/paper/2608.23101/) (2026)

## Abstract (arXiv)

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.