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Classification of Signals01:30

Classification of Signals

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification

Antonio J Torija1, Diego P Ruiz2, Angel F Ramos-Ridao3

  • 1ISVR, University of Southampton, Highfield Campus, SO17 1BJ Southampton, UK.

The Science of the Total Environment
|September 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an automated urban soundscape classification model using machine learning. The Sequential Minimal Optimization (SMO) model achieved 91.3% accuracy, outperforming standard Support Vector Machines (SVM) for better urban planning.

Keywords:
Acoustical assessmentClassification modelSequential Minimal OptimizationSoundscape classifierSoundscape evaluationSupport Vector Machines

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Area of Science:

  • Environmental Science
  • Acoustics
  • Urban Planning
  • Computer Science

Background:

  • Effective urban soundscape management requires reliable evaluation tools.
  • Soundscape evaluation necessitates subjective and acoustical categorization.
  • Existing methods lack automated, comprehensive assessment capabilities.

Purpose of the Study:

  • To develop an automated model for urban soundscape classification.
  • To classify soundscapes based on acoustical and perceptual criteria.
  • To provide a tool for comprehensive urban soundscape evaluation.

Main Methods:

  • Implementation of two machine learning techniques: Support Vector Machines (SVM) and Sequential Minimal Optimization (SMO).
  • Training and comparison of SVM and SMO models for urban soundscape classification.
  • Evaluation of model performance based on classification accuracy.

Main Results:

  • The Sequential Minimal Optimization (SMO) model demonstrated superior performance compared to the standard Support Vector Machines (SVM) model.
  • The SMO-based classification model achieved a high accuracy rate of 91.3% in correctly classifying urban soundscape instances.
  • This indicates the effectiveness of SMO in handling the complexities of urban soundscape analysis.

Conclusions:

  • The proposed automated classification model, particularly using SMO, is a viable tool for urban soundscape management.
  • Accurate soundscape classification supports informed urban planning and adaptation to public expectations.
  • Machine learning offers a powerful approach to address the complexities of urban acoustic environments.