Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths

• URL: http://arxiv.org/abs/2105.10536v1
• Date: Fri, 21 May 2021 18:59:29 GMT
• Title: Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths
• Authors: Tony Zhang, Szymon Zmyslony, Sergei Nozdrenkov, Matthew Smith, Brandon Hopkins
• Abstract summary: Honey bees are critical to our ecosystem and food security as a pollinator, contributing 35% of our global yield. In spite of their importance, beekeeping is exclusively dependent on human labor and experience-derived bees. We develop an integrated hardware sensing system for beehive monitoring through audio and environment measurements. We show that this model performs well despite limited labels, and can learn an audio embedding that is useful for characterizing different sound profiles of beehives.
• Score: 2.2680266599208765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Honey bees are critical to our ecosystem and food security as a pollinator, contributing 35% of our global agriculture yield. In spite of their importance, beekeeping is exclusively dependent on human labor and experience-derived heuristics, while requiring frequent human checkups to ensure the colony is healthy, which can disrupt the colony. Increasingly, pollinator populations are declining due to threats from climate change, pests, environmental toxicity, making their management even more critical than ever before in order to ensure sustained global food security. To start addressing this pressing challenge, we developed an integrated hardware sensing system for beehive monitoring through audio and environment measurements, and a hierarchical semi-supervised deep learning model, composed of an audio modeling module and a predictor, to model the strength of beehives. The model is trained jointly on audio reconstruction and prediction losses based on human inspections, in order to model both low-level audio features and circadian temporal dynamics. We show that this model performs well despite limited labels, and can learn an audio embedding that is useful for characterizing different sound profiles of beehives. This is the first instance to our knowledge of applying audio-based deep learning to model beehives and population size in an observational setting across a large number of hives.

Related papers

• On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis [19.205671029694074]
  This study assesses feature representations derived from speech and general audio domains, across pre-training bandwidths of 4, 8, and 16 kHz for marmoset call-type and caller classification tasks. Results show that models with higher bandwidth improve performance, and pre-training on speech or general audio yields comparable results, improving over a spectral baseline.
  arXiv  Detail & Related papers  (2024-07-23T12:00:44Z)
• Developing an AI-based Integrated System for Bee Health Evaluation [2.000782513783418]
  Honey bees pollinate about one-third of the world's food supply. Bee colonies have alarmingly declined by nearly 40% over the past decade. This study introduces a comprehensive system consisting of bee object detection and health evaluation.
  arXiv  Detail & Related papers  (2024-01-18T14:06:29Z)
• Transferable Models for Bioacoustics with Human Language Supervision [0.0]
  BioLingual is a new model for bioacoustics based on contrastive language-audio pretraining. It can identify over a thousand species' calls across taxa, complete bioacoustic tasks zero-shot, and retrieve animal vocalization recordings from natural text queries.
  arXiv  Detail & Related papers  (2023-08-09T14:22:18Z)
• Self-Supervised Visual Acoustic Matching [63.492168778869726]
  Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
  arXiv  Detail & Related papers  (2023-07-27T17:59:59Z)
• Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research [62.997667081978825]
  Modelling of early language acquisition aims to understand how infants bootstrap their language skills. Recent developments have enabled the use of more naturalistic training data for computational models. It is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data.
  arXiv  Detail & Related papers  (2023-05-03T08:25:37Z)
• Self-supervised models of audio effectively explain human cortical responses to speech [71.57870452667369]
  We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system. We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
  arXiv  Detail & Related papers  (2022-05-27T22:04:02Z)
• Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
  We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
  arXiv  Detail & Related papers  (2021-12-07T18:50:42Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning [0.0]
  This paper outlines an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP) Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.
  arXiv  Detail & Related papers  (2021-03-12T13:50:31Z)
• Collective defense of honeybee colonies: experimental results and theoretical modeling [2.519906683279153]
  Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defense. Here, we investigate how individual bees react to different alarm pheromone concentrations, and how this evolved response-pattern leads to better coordination at the group level. We are able to reproduce the experimentally measured response-pattern of real bees, and to identify the main selection pressures that shaped it.
  arXiv  Detail & Related papers  (2020-10-14T18:00:50Z)
• Noisy Agents: Self-supervised Exploration by Predicting Auditory Events [127.82594819117753]
  We propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions. We train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration. Experimental results on Atari games show that our new intrinsic motivation significantly outperforms several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-07-27T17:59:08Z)

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.