Auto deep learning for bioacoustic signals

• URL: http://arxiv.org/abs/2311.04945v2
• Date: Tue, 26 Dec 2023 13:49:45 GMT
• Title: Auto deep learning for bioacoustic signals
• Authors: Giulio Tosato, Abdelrahman Shehata, Joshua Janssen, Kees Kamp, Pramatya Jati, Dan Stowell
• Abstract summary: This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations. Using the Western Mediterranean Wetland Birds dataset, we investigated the use of AutoKeras, an automated machine learning framework.
• Score: 2.833479881983341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against traditional manually-designed deep learning models. Using the Western Mediterranean Wetland Birds dataset, we investigated the use of AutoKeras, an automated machine learning framework, to automate neural architecture search and hyperparameter tuning. Comparative analysis validates our hypothesis that the AutoKeras-derived model consistently outperforms traditional models like MobileNet, ResNet50 and VGG16. Our approach and findings underscore the transformative potential of automated deep learning for advancing bioacoustics research and models. In fact, the automated techniques eliminate the need for manual feature engineering and model design while improving performance. This study illuminates best practices in sampling, evaluation and reporting to enhance reproducibility in this nascent field. All the code used is available at https: //github.com/giuliotosato/AutoKeras-bioacustic Keywords: AutoKeras; automated deep learning; audio classification; Wetlands Bird dataset; comparative analysis; bioacoustics; validation dataset; multi-class classification; spectrograms.

Related papers

• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• A Data-Driven Analysis of Robust Automatic Piano Transcription [16.686703489636734]
  Recent developments have focused on adapting new neural network architectures to yield more accurate systems. We show how these models can severely overfit to acoustic properties of the training data. We achieve state-of-the-art note-onset accuracy of 88.4 F1-score on the MAPS dataset, without seeing any of its training data.
  arXiv  Detail & Related papers  (2024-02-02T14:11:23Z)
• AutoCure: Automated Tabular Data Curation Technique for ML Pipelines [0.0]
  We present AutoCure, a novel and configuration-free data curation pipeline. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction. In practice, AutoCure can be integrated with open source tools to promote the democratization of machine learning.
  arXiv  Detail & Related papers  (2023-04-26T15:51:47Z)
• Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition [11.489161072526677]
  We investigate robustness properties of pre-trained neural models for automatic speech recognition. In this work, we perform a robustness analysis of the pre-trained neural models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets.
  arXiv  Detail & Related papers  (2022-08-17T20:00:54Z)
• ALBench: A Framework for Evaluating Active Learning in Object Detection [102.81795062493536]
  This paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection. Developed on an automatic deep model training system, this ALBench framework is easy-to-use, compatible with different active learning algorithms, and ensures the same training and testing protocols.
  arXiv  Detail & Related papers  (2022-07-27T07:46:23Z)
• Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding [18.327196310636864]
  We introduce the correntropy learning framework into the automatic relevance determination based sparse classification model. We evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and a functional magnetic resonance imaging (fMRI) dataset.
  arXiv  Detail & Related papers  (2022-07-20T06:49:23Z)
• Revisiting Classifier: Transferring Vision-Language Models for Video Recognition [102.93524173258487]
  Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. In this study, we focus on transferring knowledge for video classification tasks. We utilize the well-pretrained language model to generate good semantic target for efficient transferring learning.
  arXiv  Detail & Related papers  (2022-07-04T10:00:47Z)
• AutoRec: An Automated Recommender System [44.11798716678736]
  We present AutoRec, an open-source automated machine learning (AutoML) platform extended from the ecosystem. AutoRec supports a highly flexible pipeline that accommodates both sparse and dense inputs. Experiments conducted on the benchmark datasets reveal AutoRec is reliable and can identify models which resemble the best model without prior knowledge.
  arXiv  Detail & Related papers  (2020-06-26T17:04:53Z)
• Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation [97.42894942391575]
  We propose FAST-DAD to distill arbitrarily complex ensemble predictors into individual models like boosted trees, random forests, and deep networks. Our individual distilled models are over 10x faster and more accurate than ensemble predictors produced by AutoML tools like H2O/AutoSklearn.
  arXiv  Detail & Related papers  (2020-06-25T09:57:47Z)
• Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL [53.40030379661183]
  Auto-PyTorch is a framework to enable fully automated deep learning (AutoDL) It combines multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) We show that Auto-PyTorch performs better than several state-of-the-art competitors on average.
  arXiv  Detail & Related papers  (2020-06-24T15:15:17Z)
• AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction [75.16836697734995]
  We propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS) AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service.
  arXiv  Detail & Related papers  (2020-03-25T06:53:54Z)

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.