## Optimizing the Half-band Filters in Multistage Decimation and Interpolation

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This article discusses a not so well-known rule regarding the filtering in multistage decimation and interpolation by an integer power of two.

## Implementing Simultaneous Digital Differentiation, Hilbert Transformation, and Half-Band Filtering

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Recently I've been thinking about digital differentiator and Hilbert transformer implementations and I've developed a processing scheme that may be of interest to the readers here on dsprelated.com.

## A New Contender in the Digital Differentiator Race

This blog proposes a novel differentiator worth your consideration. Although simple, the differentiator provides a fairly wide 'frequency range of linear operation' and can be implemented, if need be, without performing numerical multiplications.

## The World's Most Interesting FIR Filter Equation: Why FIR Filters Can Be Linear Phase

This article discusses a little-known filter characteristic that enables real- and complex-coefficient tapped-delay line FIR filters to exhibit linear phase behavior. That is, this article answers the question: What is the constraint on real- and complex-valued FIR filters that guarantee linear phase behavior in the frequency domain?

## Correcting an Important Goertzel Filter Misconception

Correcting an Important Goertzel Filter Misconception

## Complex Down-Conversion Amplitude Loss

This article illustrates the signal amplitude loss inherent in a traditional complex down-conversion system. (In the literature of signal processing, complex down-conversion is also called "quadrature demodulation.")

## Convolution DSP tutorial

Tutorial on convolution and basic DSP.

## Specifying the Maximum Amplifier Noise When Driving an ADC

I recently learned an interesting rule of thumb regarding the use of an amplifier to drive the input of an analog to digital converter (ADC). The rule of thumb describes how to specify the maximum allowable noise power of the amplifier.

## Towards Efﬁcient and Robust Automatic Speech Recognition: Decoding Techniques and Discriminative Training

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Automatic speech recognition has been widely studied and is already being applied in everyday use. Nevertheless, the recognition performance is still a bottleneck in many practical applications of large vocabulary continuous speech recognition. Either the recognition speed is not sufﬁcient, or the errors in the recognition result limit the applications. This thesis studies two aspects of speech recognition, decoding and training of acoustic models, to improve speech recognition performance in different conditions.