A Review of Artificial Intelligence (AI) Algorithms for Sound Classification: Implications for Human-Robot Interaction (HRI)

Abstract

This report presents a review of artificial intelligence (AI) algorithms and their application to audition in a human-robot interaction (HRI) context. The AI algorithms selected for auditory perception ultimately have an impact on computational transparency, system behavior explain ability, and ultimately, the quality of the HRI. AI algorithms applied to auditory perception include sounds sensed and processed by a software system, as well as sounds emitted by a software system that are meant to be recognized by a human listener. Some major classes of AI algorithms, specifically neural networks, deep learning, hidden Markov models, and hybrid models will be reviewed in the context of machines sound processing. Additionally, the effects of each class of algorithm on transparency and HRI will be discussed. Recent work in AI algorithm development suggests that hybrid models may be the best approach for sound processing as they are recommended for complex data processing and decision-making. Hybrid models blend approaches to maximize the benefits while minimizing the limitations of multiple techniques. A set of general recommendations are included in the final section of the report.

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Document Details

Document Type
Technical Report
Publication Date
Jan 23, 2020
Accession Number
AD1090606

Entities

People

  • Kelly Dickerson
  • Troy Kelley

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Artificial Neural Networks
  • Automated Speech Recognition
  • Autonomous Systems
  • Cognitive Science
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Computers
  • Deep Learning
  • Digital Data
  • Feature Extraction
  • Hidden Markov Models
  • Information Systems
  • Machine Learning
  • Markov Models
  • Models
  • Neural Networks
  • Ontologies
  • Perception
  • Probability
  • Psychology
  • Signal Processing
  • Situational Awareness
  • Unmanned Vehicles

Fields of Study

  • Computer science

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Human-Robot Interaction