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Passive Filters

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff frequency...

Active Filters

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:

The Cochlea

The Cochlea

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Parallel Resonance

Parallel Resonance

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Related Experiment Video

Updated: May 12, 2026

Performing Repeated Intraoperative Impedance Telemetry Measurements during Cochlear Implantation

Performing Repeated Intraoperative Impedance Telemetry Measurements during Cochlear Implantation

Published on: August 4, 2023

A beamformer post-filter for cochlear implant noise reduction.

Adam A Hersbach¹, David B Grayden, James B Fallon

¹NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia. ahersbach@cochlear.com

The Journal of the Acoustical Society of America

|April 6, 2013

Summary

This study introduces a new multi-microphone noise reduction algorithm for cochlear implant users. The novel approach significantly improves speech understanding in complex noisy environments compared to traditional beamforming methods.

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Area of Science:

Audiology
Signal Processing
Biomedical Engineering

Background:

Cochlear implant (CI) users struggle with speech comprehension in noisy settings.
Existing multi-microphone noise reduction often relies on beamforming, which is less effective with multiple or diffuse noise sources.

Purpose of the Study:

To develop and evaluate a novel multi-microphone noise reduction algorithm for CI users.
To improve speech intelligibility in challenging acoustic environments.

Main Methods:

A new algorithm employing spatial filtering to estimate signal-to-noise ratio (SNR).
Attenuation of time-frequency components with poor SNR.
Evaluation with 12 CI users in a 4-talker babble noise condition with dynamic speaker locations.

Main Results:

The proposed algorithm achieved a significant mean improvement of 4.6 dB in speech reception threshold (SRT).
Performance was superior to adaptive beamforming, especially in spatially separated and dynamic noise conditions.

Conclusions:

The developed multi-microphone algorithm offers substantial benefits for CI users in complex noisy environments.
Spatial filtering and SNR estimation provide an effective strategy for noise reduction in hearing devices.