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Related Concept Videos

Passive Filters01:27

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...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Active Filters01:25

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:
Impulse Response01:17

Impulse Response

The impulse response is the system's reaction to an input impulse. In an RC circuit, the voltage source is the input, and the capacitor's voltage is the output. The system's state and output response before and after input excitation are distinctly defined.
Kirchhoff's law forms an input signal equation, with the capacitor's current and voltage providing the output. Substituting the current and dividing by RC yields a differential equation. The output for an impulse input is the impulse...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Sound as Pressure Waves01:17

Sound as Pressure Waves

Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...

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Related Experiment Video

Updated: Jun 24, 2026

Pupillometry to Assess Auditory Sensation in Guinea Pigs
09:25

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Published on: January 6, 2023

A Dynamic Compressive Gammachirp Auditory Filterbank.

Toshio Irino1, Roy D Patterson

  • 1Faculty of Systems Engineering, Wakayama University, Wakayama 640-8510, Japan (e-mail: irino@sys.wakayama-u.ac.jp ).

IEEE Transactions on Audio, Speech, and Language Processing
|March 31, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonlinear auditory filterbank, the dynamic compressive gammachirp (dcGC) filter, for audio signal processing. This nonlinear approach better models human hearing, improving audio processing systems.

Related Experiment Videos

Last Updated: Jun 24, 2026

Pupillometry to Assess Auditory Sensation in Guinea Pigs
09:25

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Published on: January 6, 2023

Area of Science:

  • Auditory signal processing
  • Psychoacoustics
  • Digital signal processing

Background:

  • Traditional audio signal processors often use linear filterbanks, which do not fully capture the level-dependent nature of human auditory processing.
  • Existing nonlinear auditory filter models were limited and lacked analysis/synthesis schemes.

Purpose of the Study:

  • To develop a nonlinear analysis/synthesis filterbank system that accurately models human auditory processing.
  • To extend the gammatone filter concept to a compressive gammachirp (cGC) auditory filter.
  • To incorporate temporal dynamics into the nonlinear filterbank for speech processing applications.

Main Methods:

  • Developed a compressive gammachirp (cGC) auditory filter.
  • Designed a fast-acting level control circuit for the cGC filter to create a dynamic version (dcGC).
  • Utilized psychophysical data on tone-in-noise masking, compression, and two-tone suppression to estimate dcGC filter parameters.

Main Results:

  • Successfully implemented a nonlinear filterbank based on the dynamic compressive gammachirp (dcGC) filter.
  • The dcGC filter effectively models psychophysical phenomena like compression and two-tone suppression.
  • The developed system allows for the incorporation of temporal dynamics crucial for speech processing.

Conclusions:

  • The dcGC filterbank provides a more biologically plausible model of auditory processing compared to linear filterbanks.
  • This nonlinear approach enables the development of advanced audio signal processors that better mimic human hearing.
  • Analysis/synthesis systems using dcGC filterbanks can leverage existing algorithms developed for linear systems.