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Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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Related Experiment Video

Updated: Jan 13, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Parallel Time-Frequency Multi-Scale Attention with Dynamic Convolution for Environmental Sound Classification.

Hongjie Wan1, Hailei He1, Yuying Li1

  • 1Information Engineering Department, Beijing University of Chemical Technology, No. 15 North Third Ring Road East, Beijing 100029, China.

Entropy (Basel, Switzerland)
|October 28, 2025
PubMed
Summary

This study introduces a novel parallel time-frequency multi-scale attention (PTFMSA) module and network (PTFMSAN) for environmental sound classification (ESC). PTFMSAN achieves 90% accuracy, outperforming baseline models by effectively handling frequency shift-invariance and multi-scale features.

Keywords:
CNNdeep learningdynamic convolutionenvironmental sound classificationmulti-scale convolution

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Convolutional neural networks (CNNs) are standard for environmental sound classification (ESC).
  • Traditional 2-D convolutions exhibit limitations due to assumptions of translation invariance in both time and frequency, which do not hold true for the frequency dimension.
  • Single-scale convolutions restrict the receptive field, hindering comprehensive feature extraction.

Purpose of the Study:

  • To introduce a novel parallel time-frequency multi-scale attention (PTFMSA) module to enhance CNNs for ESC.
  • To develop a compact network, PTFMSAN, capable of processing raw waveforms directly for improved environmental sound classification.
  • To address the limitations of frequency shift-invariance and limited receptive fields in existing CNN models.

Main Methods:

  • Development of the parallel time-frequency multi-scale attention (PTFMSA) module, integrating local and global attention across multiple scales.
  • Implementation of a parallel branch structure to prevent interference between time and frequency domain feature extraction.
  • Utilization of learnable parameters for dynamic weight adjustment of different branches during training.
  • Application of between-class (BC) training to further enhance learning.

Main Results:

  • The proposed PTFMSAN network achieved a classification accuracy of 90% on the ESC-50 dataset.
  • PTFMSAN demonstrated superior performance compared to the baseline model.
  • Ablation experiments confirmed the effectiveness of individual modules within the PTFMSAN architecture.

Conclusions:

  • The PTFMSA module and PTFMSAN network offer a significant advancement in environmental sound classification.
  • The proposed methods effectively address frequency shift-invariance and multi-scale feature representation challenges.
  • PTFMSAN provides a competitive and accurate solution for environmental sound classification tasks.