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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:
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Passive Filters01:27

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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.
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In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
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An RLC circuit combines a resistor, inductor, and capacitor, connected in a series or parallel combination.
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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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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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Attentive Recurrent Network for Low-Latency Active Noise Control.

Hao Zhang1, Ashutosh Pandey1, DeLiang Wang1,2

  • 1Department of Computer Science and Engineering, The Ohio State University, USA.

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Summary
This summary is machine-generated.

This study introduces deep active noise control (ANC) strategies to minimize processing latency. By combining methods like attentive recurrent networks and delay-compensated training, researchers achieved zero or negative algorithmic latency in ANC systems.

Keywords:
Active noise controlalgorithmic delayattentive recurrent network (ARN)deep ANClow-latency

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Processing latency is a critical challenge for active noise control (ANC) systems due to causality constraints.
  • Deep learning frameworks offer potential for advanced ANC but can introduce significant latency.

Purpose of the Study:

  • To develop and evaluate low-latency active noise control within a deep learning framework (deep ANC).
  • To reduce algorithmic latency in deep ANC systems without compromising performance.

Main Methods:

  • Employed a time-domain attentive recurrent network for deep ANC with reduced frame sizes.
  • Introduced a delay-compensated training strategy for ANC using predicted noise.
  • Utilized a revised overlap-add method for signal resynthesis to minimize frame overlap latency.

Main Results:

  • The proposed strategies effectively reduce algorithmic latency in deep ANC.
  • Combining the strategies achieved zero or even negative algorithmic latency.
  • ANC performance was maintained without significant degradation.

Conclusions:

  • The developed strategies are effective for achieving low-latency deep ANC.
  • It is possible to achieve near-zero or negative algorithmic latency in ANC systems.
  • Deep learning can be practically applied to real-time ANC with latency mitigation.