DSP Spreadsheet: Frequency Mixing ^NEW^

DSP Spreadsheet: Frequency Mixing ^NEW^

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Zynq RFSoC DFE is the latest adaptive RFSoC platform that integrates more hardened IP than soft logic for critical DFE processing. Enabling a flexible solution for 5G New Radio, Zynq RFSoC DFE operates up to 7.125GHz of input/output frequency with power-efficiency and cost-effectiveness.

With incredibly easy-to-use and highly accurate predictive software [DISPLAY] doing all the grunt work with the system, the FOH engineer retains full artistic control and can enjoy mixing the show in the full knowledge that the sound created at the mix position will be heard everywhere throughout the audience.

The loudspeaker shall have 90° horizontal dispersion and 7.5° vertical dispersion. Vertical dispersion of the complete array shall be determined by a combination of the splay angles between adjacent enclosures and dedicated array control software. The on-axis frequency response shall be 52Hz-18kHz +/- 3dB and the loudspeaker shall produce a maximum SPL of 139dB peak (LF), 140dB peak (MF) and 145dB peak (HF) at 1 metre.

Your first clue that these abominations suck are the instrument frequency charts that purport to show the fundamental frequency ranges as well as the harmonics of every common instrument you might mix.

In the cases where the information is accurate, it's usually unusable or should be avoided. Below, we'll show you a few examples of these kinds of audio frequency charts and explain the problems with them.

Lots of newcomers to the world of equalization and mixing encounter these charts, and we need to set the record straight. The main issue with these types of charts are the effect they have on the amateur mixer who honestly is attempting to learn and increase his or her skill level.

An EQ cheat sheet, also called an instrument frequency chart or an audio frequency chart, is an infographic that displays the supposed frequency responses of every common instrument laid out across the frequency range of human hearing.

I'll admit that this one simply presents information without making any suggestions about using it for equalization purposes. It's just sharing instrument frequency ranges in a pretty fashion. You can't hurt yourself too bad by reading this one.

Instead of harping on what it does wrong (like being an eyesore), we can at least point out one thing they did right that we'll discuss later, which is assigning these subjective names for tones to specific frequency ranges like they should be, instead of acting as if they change and move around depending on the instrument.

It doesn't really matter how any instrument sounds in isolation. The only thing that matters is the end result of all of the tracks mixed down together, and these charts don't deal with that context at all, which is at least 90% of the art of mixing.

Consulting a chart can be okay as an absolute beginner who has no idea at all about what sounds lie in which frequency ranges. But once you get even the most basic lay of the land figured out, you need to practice by actually mixing.

By hunting for problematic frequencies in the sounds you're mixing, you're giving your brain context in which to solidify your learning. By using a bandpass filter or parametric equalizer with a tight Q boost, you can sweep up and down the frequency spectrum until the bad sound pops out at you.

All you really want to do is to be able to hear a frequency that you like or don't like and be able to identify which range it falls in in the table below. If you can do that, you're golden in terms of EQ speed and skill.

Few instruments besides bass guitars and other bass-focused instruments reach this deep into the frequency spectrum. You can remove a lot of noise and rumbles from a mix by high-pass filtering most tracks to cut out this region.

Don't get trapped or thrown off course by these charts. Unless you've never spent a single moment considering that low frequency sounds occur in the bass region and high frequency sounds occur in the upper regions, then you have zero reason to look at these charts.

Yeah, they're pretty, but that's about it. Nothing will or can replace the time you spend in front of your monitors actually mixing and listening with your ears. These EQ cheat sheets want you to use your eyes, which is an absolute no-go.

As you can see in the chart below all the instruments have a specific place in the frequency spectrum. The blue color represents the fundamental frequencies of each specific instrument while the reds signify their harmonics. Low fundamentals are the blacks on the left while the black surrounded by reds represents Air.

New for August 2021. Spectral inversion happens when you mix an RF signal with a high-side local oscillator. "High side" in this context means that the LO signal is higher in frequency than the RF signal. As you know, any signal of interest has a certain amount of bandwidth. If you are operating a radar and looking for a Doppler shift, high-side mixing will invert the Doppler so that objects approaching will be shifted down in frequency (opposite of normal), and objects receding will be shifted up. Of course, you can call on your digital people to just deal with it....

those coefficients are calculated with the awesome biquad calculation spreadsheet from the MiniDSP website. bear in mind - the spreadsheet & Sigmastudio use different definitions of coefficients a1 and a2. multiply the spreadsheet's a1 and a2 values by -1 before plugging them into Sigmastudio. the end result is a precise bass-boost in the frequency response of the DSP:

manually equalizing the room feels ridiculous. i don't think it is a good task for a human. deciding exactly where and how to adjust the frequency response curve seems like an optimization job for a computer, and i'll probably turn that into a future project.

For the parameters considered below, the chain is assumed to contain a cascade of devices, which are (nominally) impedance matched. The procedures given here allow all calculations to be displayed in the spreadsheet in sequence and no macros are used. Although this makes for a longer spreadsheet, no calculations are hidden from the user.For convenience, the spread sheet columns, show the frequency in sub-bands, with bandwidths sufficiently narrow to ensure that any gain ripple is sufficiently characterized.

In the spread sheet, the total frequency band of interest B(Hz) is divided into M sub-bands (spreadsheet columns) of B/M (Hz) each, and for each sub-band (m = 1 to M) the thermal noise power is derived, as described above. In practice, these results will differ slightly, from column to column, if the system has gain ripple.

The presence of a mixer in an RF chain complicates the spreadsheet because the frequency range at the output differs from that at the input. In addition, because mixers are non-linear devices, they introduce many inter-modulation products, which are undesirable, especially in wide-band systems.

A similar conversion can be done using mathematical methods on the same sound waves or virtually any other fluctuating signal that varies with respect to time. The Fourier transform is the mathematical tool used to make this conversion. Simply stated, the Fourier transform converts waveform data in the time domain into the frequency domain. The Fourier transform accomplishes this by breaking down the original time-based waveform into a series of sinusoidal terms, each with a unique magnitude, frequency, and phase. This process, in effect, converts a waveform in the time domain that is difficult to describe mathematically into a more manageable series of sinusoidal functions that when added together, exactly reproduce the original waveform. Plotting the amplitude of each sinusoidal term versus its frequency creates a power spectrum, which is the response of the original waveform in the frequency domain. Figure 1 illustrates this time to frequency domain conversion concept.

The Fourier transform has become a powerful analytical tool in diverse fields of science. In some cases, the Fourier transform can provide a means of solving unwieldy equations that describe dynamic responses to electricity, heat or light. In other cases, it can identify the regular contributions to a fluctuating signal, thereby helping to make sense of observations in astronomy, medicine and chemistry. Perhaps because of its usefulness, the Fourier transform has been adapted for use on the personal computer. Algorithms have been developed to link the personal computer and its ability to evaluate large quantities of numbers with the Fourier transform to provide a personal computer-based solution to the representation of waveform data in the frequency domain. But what should you look for in Fourier analysis software? What makes one software package better than another in terms of features, flexibility, and accuracy? This application note will present and explain some of the elements of such software packages in an attempt to remove the mystery surrounding this powerful analytical tool.

The transformation from the time domain to the frequency domain is reversible. Once the power spectrum is displayed by one of the two previously mentioned transforms, the original signal can be reconstructed as a function of time by computing the inverse Fourier transform (IFT). Each of these transforms will be discussed individually in the following paragraphs to fill in missing background and to provide a yardstick for comparison among the various Fourier analysis software packages on the market.

But the increase in speed comes at the cost of versatility. The FFT function automatically places some restrictions on the time series to be evaluated in order to generate a meaningful, accurate frequency response. Because the FFT function uses a base 2 logarithm by definition, it requires that the range or length of the time series to be evaluated contains a total number of data points precisely equal to a 2-to-the-nth-power number (e.g., 512, 1024, 2048, etc.). Therefore, with an FFT you can only evaluate a fixed length waveform containing 512 points, or 1024 points, or 2048 points, etc. For example, if your time series contains 1096 data points, you would only be able to evaluate 1024 of them at a time using an FFT since 1024 is the highest 2-to-the-nth-power that is less than 1096. 2b1af7f3a8