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

• URL: http://arxiv.org/abs/2407.21453v2
• Date: Wed, 11 Sep 2024 08:07:24 GMT
• Title: TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
• Authors: Zhaolan Huang, Adrien Tousnakhoff, Polina Kozyr, Roman Rehausen, Felix Bießmann, Robert Lachlan, Cedric Adjih, Emmanuel Baccelli,
• Abstract summary: Monitoring biodiversity at scale is challenging. detecting and identifying species in fine grained requires highly accurate machine learning (ML) methods. deploying these models to low power devices requires novel compression techniques and model architectures.
• Score: 1.0790796076947324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• AudioProtoPNet: An interpretable deep learning model for bird sound classification [1.6298921134113031]
  We present an adaption of the Prototypical Part Network (ProtoPNet) for audio classification that provides inherent interpretability through its model architecture. Our approach is based on a ConvNeXt backbone architecture for feature extraction and learns patterns for each bird species using spectrograms of the training data.
  arXiv  Detail & Related papers  (2024-04-16T09:37:41Z)
• Exploring Meta Information for Audio-based Zero-shot Bird Classification [113.17261694996051]
  This study investigates how meta-information can improve zero-shot audio classification. We use bird species as an example case study due to the availability of rich and diverse meta-data.
  arXiv  Detail & Related papers  (2023-09-15T13:50:16Z)
• Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks [7.358050500046429]
  We study the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model.
  arXiv  Detail & Related papers  (2023-03-06T17:53:42Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Few-Shot Non-Parametric Learning with Deep Latent Variable Model [50.746273235463754]
  We propose Non-Parametric learning by Compression with Latent Variables (NPC-LV) NPC-LV is a learning framework for any dataset with abundant unlabeled data but very few labeled ones. We show that NPC-LV outperforms supervised methods on all three datasets on image classification in low data regime.
  arXiv  Detail & Related papers  (2022-06-23T09:35:03Z)
• Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural Networks [0.0]
  Automated species detection from passively recorded soundscapes via machine-learning approaches is a promising technique. We use soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN) created with transfer learning. Our results suggest that transfer learning and data augmentation can make the use of CNNs to classify species' vocalizations feasible even for small soundscape-based projects with many rare species.
  arXiv  Detail & Related papers  (2021-11-29T21:34:57Z)
• Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks [11.099308746733028]
  We study the classification of animal behavior using accelerometry data through various recurrent neural network (RNN) models. We evaluate the classification performance and complexity of the considered models. We also include two state-of-the-art convolutional neural network (CNN)-based time-series classification models in the evaluations.
  arXiv  Detail & Related papers  (2021-11-24T23:28:25Z)
• A deep neural network for multi-species fish detection using multiple acoustic cameras [0.0]
  We present a novel approach that takes advantage of both CNN (Convolutional Neural Network) and classical CV (Computer Vision) techniques. The pipeline pre-treats the acoustic images to extract 2 features, in order to localise the signals and improve the detection performances. The YOLOv3-based model was trained with data of fish from multiple species recorded by the two common acoustic cameras.
  arXiv  Detail & Related papers  (2021-09-22T11:47:24Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.