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Related Concept Videos

Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Parallel Resonance01:23

Parallel Resonance

The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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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.
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The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
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Noise reduction utilizing cross time-frequency epsilon-filter.

Tomomi Abe1, Mitsuharu Matsumoto, Shuji Hashimoto

  • 1Pure and Applied Physics, Waseda University, 55N-4F-10A, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan. tomomi@shalab.phys.waseda.ac.jp

The Journal of the Acoustical Society of America
|May 12, 2009
PubMed
Summary

A new cross time-frequency epsilon-filter (TF epsilon-filter) enhances noise reduction by applying filters along both time and frequency axes. This advanced method effectively reduces noise without distorting frequently varying signals.

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Area of Science:

  • Signal Processing
  • Digital Audio Processing
  • Noise Reduction Techniques

Background:

  • The time-frequency epsilon-filter (TF epsilon-filter) is effective for noise reduction in signals with frequent variations, like speech.
  • However, TF epsilon-filter can cause signal distortion when applied to noise that varies rapidly along the time axis.

Purpose of the Study:

  • To introduce an advanced noise reduction method, the cross time-frequency epsilon-filter (cross TF epsilon-filter).
  • To address limitations of the standard TF epsilon-filter in handling noise with high temporal variability.

Main Methods:

  • Developed a cross TF epsilon-filter that applies the epsilon-filter to complex spectra along both time and frequency axes.
  • The filter is designed to reduce noise where neighboring frequency bins exhibit similar power levels.

Main Results:

  • The cross TF epsilon-filter demonstrates effectiveness in noise reduction, particularly for complex noise scenarios.
  • Comparative experiments validate the method's performance against existing techniques, assessing noise reduction efficiency and robustness.

Conclusions:

  • The proposed cross TF epsilon-filter offers improved noise reduction capabilities compared to the standard TF epsilon-filter.
  • This method effectively reduces noise without significant signal distortion, even in challenging noise conditions.