Read more about the article Audio Equalisation Filter & Parametric Filtering

Audio Equalisation Filter & Parametric Filtering

When working with audio signals a common requirement is to be able to equalise, cut or boost various frequency bands. A large number of hardware devices on the market provide this capability. The key aspect is that such filters are able to control bandwidth, centre frequency and gain separately. There are broadly two classes of filter used, a “shelving” filter and an “equalising “filter (also known as a “peak” filter). A shelving filter is akin to low pass and high pass filters. An equalising filter is like a bandpass or band reject filter.

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Cleaning Up Data

When we have a very noisy signal with a large number of spikes and signal bursts then if all else fails try Median Filtering. This is a technique often used in cleaning up pictures. The operation is almost childishly simple in concept but we will save the details until we have examined an example.

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Read more about the article What Is A Fourier Transform?

What Is A Fourier Transform?

A Fourier Transform takes a signal and represents it either as a series of cosines (real part) and sines (imaginary part) or as a cosine with phase (modulus and phase form). As an illustration, we will look at Fourier analysing the sum of the two sine waves shown below. The resultant summed signal is shown in the third graph.

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Read more about the article Data Smoothing : RC Filtering And Exponential Averaging

Data Smoothing : RC Filtering And Exponential Averaging

[Updated 12th March 2013]

What are RC Filtering and Exponential Averaging and how do they differ? The answer to the second part of the question is that they are the same process! If one comes from an electronics background then RC Filtering (or RC Smoothing) is the usual expression. On the other hand an approach based on time series statistics has the name Exponential Averaging, or to use the full name Exponential Weighted Moving Average. This is also variously known as EWMA or EMA. (more…)

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Spectrum Smoothing : Why and How?

Sometimes data has spikes which are clearly artefacts of the processing or are due to some other external source. One is used to seeing these on time series but in some cases there are unrepresentative “spikes” in the frequency analysed data. Here we discuss how we can use spectrum smoothing to alleviate the problem. An example spectrum is shown below.

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Read more about the article Measuring Torsional Twist & Vibration Along a Shaft or Through a Geartrain

Measuring Torsional Twist & Vibration Along a Shaft or Through a Geartrain

The measurement of torsional twist, or the twist angle, between two points along a shaft or through a gear train may be derived from a pair of tacho signals, one at each end of the shaft. Typically the tacho signals would be derived from gear teeth giving a known number of pulses/revolution. For example one end of a shaft could have a gear wheel with say 60 teeth giving 60 pulses/revolutions when measured with say an inductive or eddy current probe. (more…)

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Read more about the article Order Tracking, Frequency and Hertz

Order Tracking, Frequency and Hertz

In this article, we look at the relationships between frequency, the unit Hertz and order tracking. The most common form of digitising data is to use a regular time-based method. Data is sampled at a constant rate specified as a number of samples/second. The Nyquist frequency, fN, is defined such that fN = SampleRate/2. As discussed elsewhere, Shannon’s Sampling Theorem tells us that if the signal we are sampling is band limited so that all the information is at frequencies less than fN then we are alias free and have a valid digitised signal. Furthermore, the theorem assures us that we have all the available information on the signal.

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Read more about the article Dynamic Range And Overall Level : What Are They ?

Dynamic Range And Overall Level : What Are They ?

Accurate measurement of a signal depends on the dynamic range and the overall level of the data acquisition system. The overall level setting may be thought of as determining the largest signal that can be measured. This clearly depends on the present gain setting. That is the overall level is related to the gain. Clearly if the overall level is too small (gain too high) then the signal will be clipped and we will have poor quality data. The dynamic range then tells us that for the given overall level what is the smallest signal we can measure accurately whilst simultaneously measuring the large signal.

In a very simple sense suppose we have an artificial signal which consists of a sinewave at a large amplitude A for the first half and that this is followed by a sinewave with a small amplitude a for the second half. We will set the gain (the overall level) to allow the best measurement of the A sinewave. The dynamic range tells us how small a may be so we can also measure that without changing settings.
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Read more about the article High Pass Filtering And Tacho Signals

High Pass Filtering And Tacho Signals

It is sometimes necessary to perform high pass filtering to eliminate low frequency signals. These may arise for instance from whole body vibrations when perhaps our interest is in higher frequency components from a substructure such as an engine or gearbox mounting. The vibration levels are speed sensitive and the usual scheme is to record a once per revolution ‘tacho’ signal with the vibration data. The tacho signal, which ideally is a nice regular pulse train, is processed to find rotational speed and hence to select which part of the vibration signal is to be frequency analyzed. The most common form of analysis is a waterfall type such as shown below.

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Read more about the article Don’t Let Spikes Spoil Your Data

Don’t Let Spikes Spoil Your Data

In many real-world applications it is impossible to avoid “spikes” or “dropouts” in data that we record. Many people assume that these only cause problems with their data if they become obvious. This is not always the case. Consider the following two time histories.

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Read more about the article Non Linear Calibration Curve And Polynomial

Non Linear Calibration Curve And Polynomial

Not all systems vary linearly. One very well known case is, of course, thermocouples. International standard curves are available for these so they present little difficulty. The issue discussed here is determining a non linear calibration curve and if appropriate reducing to a polynomial.

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Read more about the article A Weighting. And B. And C.

A Weighting. And B. And C.

Some devices, particularly digital tape recorders, apply A-weighting to all their data to achieve acceptable data compression. This is fine unless you want to analyse the unweighted data or apply a different weighting factor. Using Prosig’s DATS software, it is a simple task to instruct the WEIGHT module to either unweight the data or remove one weighting factor and apply another.

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Read more about the article Understanding The Cross Correlation Function

Understanding The Cross Correlation Function

To illustrate the use of the cross correlation function, a source location example is shown below. For this, it is assumed that there is a noise source at some unknown position between 2 microphones. A cross correlation technique and a transfer function like approach were used to determine the location. (more…)

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Read more about the article How Do I Downsample Data?

How Do I Downsample Data?

Sometimes we have digitised data at a much higher rate than we need. How can we downsample data? If I wanted to say halve the sample rate can I just throw away every other data point?

The answer is NO, except in pathological conditions where you know that there is no frequency content above the new Nyquist frequency.

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Read more about the article Does The Signal Have A Gaussian Probability Density?

Does The Signal Have A Gaussian Probability Density?

The PROB module in DATS for Windows provides, amongst other options, a probability density analysis. Also, the signal generation suite has a module, GENPRB, which generates a classical gaussian probability density curve (and others). How then may these be used to compare the probability density of our measured signal with that of a true Gaussian one. The method is quite straight forward and is a matter of scaling.

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Read more about the article Removing Phase Delay Using A Phaseless Filter

Removing Phase Delay Using A Phaseless Filter

In many instances we need to filter a signal to remove unwanted frequencies. If we use classical filters such as Butterworth, Chebyshev or even Bessel then a phase delay is introduced. This phase delay is itself a function of frequency so that the signal content at one frequency is delayed a different amount to that at another frequency. Why does this matter?

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Read more about the article Methods To Remove Spikes From Data

Methods To Remove Spikes From Data

For various reasons data captured in the real world often contains spikes that will give erroneous results when analysed. The DATS software package provides various ways of editing and to remove spikes from data. Let us consider a real life case history.

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Read more about the article Time Varying Overall Level Vibration (or Noise)

Time Varying Overall Level Vibration (or Noise)

A common requirement to measure overall level vibration (or noise) as a function of time. Now, the overall level is a measure of the total dynamic energy in the signal. That is it does not contain the energy due to the DC level, which is the same as the mean value. The overall level is often loosely referred to as the signal RMS value. However the formal definition of the RMS level is that it contains the DC level as well as the dynamic energy level. If only the dynamic contribution is required then the measure needed is, strictly speaking, the Standard Deviation (SD). Sometimes it is useful to refer to the SD as the Dynamic RMS.

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Read more about the article Interpretation of the Articulation Index

Interpretation of the Articulation Index

The Articulation Index or AI gives a measure of the intelligibility of hearing speech in a given noise environment. The metric was originally developed in 1949 in order to give a single value that categorised the speech intelligibility of a communication system. The basic interpretation of the AI value is the higher the value then the easier it is to hear the spoken word. The AI value is expressed either as a factor in the range zero to unity or as a percentage.

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