Improving Primate Sounds Classification using Binary Presorting for Deep Learning

• URL: http://arxiv.org/abs/2306.16054v1
• Date: Wed, 28 Jun 2023 09:35:09 GMT
• Title: Improving Primate Sounds Classification using Binary Presorting for Deep Learning
• Authors: Michael K\"olle, Steffen Illium, Maximilian Zorn, Jonas N\"u{\ss}lein, Patrick Suchostawski and Claudia Linnhoff-Popien
• Abstract summary: In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging textitComparE 2021 dataset, with the task of classifying between different primate species sounds.
• Score: 6.044912425856236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging \textit{ComparE 2021} dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines.

Related papers

• Advanced Framework for Animal Sound Classification With Features Optimization [35.2832738406242]
  We propose an automated classification framework applicable to general animal sound classification. Our approach consistently outperforms baseline methods by over 25% in precision, recall, and accuracy.
  arXiv  Detail & Related papers  (2024-07-03T18:33:47Z)
• Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
  We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
  arXiv  Detail & Related papers  (2024-06-20T18:35:47Z)
• Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification [49.09505771145326]
  We propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-04-26T06:00:27Z)
• WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database [49.1574468325115]
  We introduce textbfWhaleNet (Wavelet Highly Adaptive Learning Ensemble Network), a sophisticated deep ensemble architecture for the classification of marine mammal vocalizations. We achieve an improvement in classification accuracy by $8-10%$ over existing architectures, corresponding to a classification accuracy of $97.61%$.
  arXiv  Detail & Related papers  (2024-02-20T11:36:23Z)
• Dynamic Loss For Robust Learning [17.33444812274523]
  This work presents a novel meta-learning based dynamic loss that automatically adjusts the objective functions with the training process to robustly learn a classifier from long-tailed noisy data. Our method achieves state-of-the-art accuracy on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision.
  arXiv  Detail & Related papers  (2022-11-22T01:48:25Z)
• Learning with Neighbor Consistency for Noisy Labels [69.83857578836769]
  We present a method for learning from noisy labels that leverages similarities between training examples in feature space. We evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise.
  arXiv  Detail & Related papers  (2022-02-04T15:46:27Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• An Ensemble of Convolutional Neural Networks for Audio Classification [9.174145063580882]
  ensembles of CNNs for audio classification are presented and tested on three freely available audio classification datasets. To the best of our knowledge, this is the most extensive study investigating ensembles of CNNs for audio classification.
  arXiv  Detail & Related papers  (2020-07-15T19:41:15Z)

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.