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Echo

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...

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Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...

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Related Experiment Video

Updated: Jun 19, 2025

Microparticle Manipulation by Standing Surface Acoustic Waves with Dual-frequency Excitations

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Published on: August 21, 2018

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Eduardo Pichardo¹, Juan G Avalos², Giovanny Sánchez²

¹Tecnologico de Monterrey, School of Engineering and Sciences, Calle del Puente 222, Col. Ejidos de Huipulco Tlalpan, Ciudad de Mexico 14380, Mexico.

Biomimetics (Basel, Switzerland)

|July 26, 2024

Summary

This summary is machine-generated.

This study introduces a novel acoustic echo canceller (AEC) system using bio-inspired Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. These methods enhance convergence speed for improved performance in voice-controlled IoT devices.

Keywords:

acoustic echo canceller adaptive filtering grey wolf optimization particle swarm optimization

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Area of Science:

Signal Processing
Artificial Intelligence
Internet of Things (IoT)

Background:

Acoustic echo cancellers (AECs) are vital for voice-controlled IoT devices, but their performance degrades in noisy environments.
Conventional adaptive filtering methods show limitations in echo noise reduction effectiveness.
Bio-inspired algorithms offer faster convergence rates compared to traditional gradient optimization algorithms.

Purpose of the Study:

To develop a high-performance AEC system for IoT applications.
To improve convergence speed and tracking capabilities in echo cancellation.
To address the challenge of echo noise in real-world acoustic environments.

Main Methods:

Implementation of a novel AEC system.
Integration of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.
Evaluation of bio-inspired algorithms for enhanced echo noise reduction.

Main Results:

The proposed AEC system demonstrates a higher convergence speed compared to existing solutions.
Improved tracking capabilities in reducing echo noise.
Enhanced performance in real acoustic environments.

Conclusions:

The GWO and PSO-based AEC system offers superior performance for voice-controlled IoT devices.
Faster convergence leads to more effective echo noise reduction.
This advancement contributes to higher quality and more realistic sound in IoT applications.