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Related Concept Videos

Auditory Perception01:17

Auditory Perception

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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Convolution Properties II01:17

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Subliminal Perception01:15

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Subliminal perception refers to the processing of sensory information that occurs below the level of conscious awareness. Researchers study subliminal perception by presenting a stimulus, such as a word or image, very quickly, typically around 50 milliseconds. This rapid presentation is often followed by another stimulus, such as a pattern of dots or lines, which blocks further mental processing of the initial stimulus. As a result, if participants cannot identify the initial stimulus better...
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Factors Affecting Perception01:25

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Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
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Convolution Properties I01:20

Convolution Properties I

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Convolution computations can be simplified by utilizing their inherent properties.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition.

Honghui Yang1, Junhao Li2, Sheng Shen3

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China. hhyang@nwpu.edu.cn.

Sensors (Basel, Switzerland)
|March 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an Auditory Perception inspired Deep Convolutional Neural Network (ADCNN) for underwater acoustic target recognition. The ADCNN model effectively classifies ship-radiated noise, achieving 81.96% accuracy in complex marine environments.

Keywords:
auditory perception inspiredbrain-inspireddeep learningfilter learningship-radiated noiseunderwater acoustic target recognition

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Bio-inspired Computing

Background:

  • Underwater acoustic target recognition (UATR) is crucial but challenging due to complex marine environments.
  • Existing methods struggle with the intricacies of ship-radiated noise.
  • Neural mechanisms of auditory perception offer a promising avenue for improved UATR.

Purpose of the Study:

  • To propose a novel end-to-end deep neural network, the Auditory Perception inspired Deep Convolutional Neural Network (ADCNN), for UATR.
  • To leverage bio-inspired mechanisms for enhanced feature extraction and classification of underwater acoustic targets.
  • To demonstrate the efficacy of the ADCNN model in handling ship-radiated noise.

Main Methods:

  • Designed a bank of multi-scale deep convolution filters inspired by frequency component perception to decompose raw time-domain signals.
  • Employed plasticity-inspired random initialization and learned optimization for filter parameters.
  • Utilized max-pooling and fully connected layers for feature extraction from decomposed signals.
  • Integrated features in fusion layers for deep representation and classification of underwater acoustic targets.

Main Results:

  • The ADCNN model successfully decomposed, modeled, and classified ship-radiated noise signals.
  • Achieved a classification accuracy of 81.96%, outperforming other methods in contrast experiments.
  • Demonstrated efficient processing of complex underwater acoustic data.

Conclusions:

  • The proposed ADCNN model effectively simulates deep acoustic information processing structures found in the auditory system.
  • Auditory perception-inspired deep learning methods show significant potential for improving UATR performance.
  • The ADCNN offers a robust solution for classifying underwater acoustic targets in challenging conditions.