![]() Unfortunately, making a recording louder causes problems too, such as decreasing the dynamic range and introducing distortions. This is also one of the reasons behind the so-called loudness war, a term that refers to artists and studio engineers trying to burn the loudest levels into a recording: being louder than competition creates the illusion that music sounds better, because of the more prominent bass and treble, which is difficult not to like. This is the reason why the loudness button has been invented: to artificially compensate for the drop lower and upper ends which naturally occur at quieter levels. In practice, this means that the same music only played quieter will sound duller. They show the sound pressure levels to effectively apply in order to achieve a given perceived level. ![]() This effect is described by the so-called Fletcher-Munson curves. Exposed to loud music, the frequency response of our ears becomes somehow flatter. That's why the test above worked better at quieter levels. The difference in frequency sensitivity mentioned above, actually depends on sound levels! At lower levels, our ear sensitivity in the bass and treble areas, drops significantly. The last effect is probably the most evil for audio enthusiasts. Variations of the Frequency Sensitivity depending on the level Also note that sound pressure levels are often expressed in decibels (dB), a unit that is intrinsically logarithmic: a linear scale in dB is exponential in terms of acoustic pressure levels. It comes from the fact that it takes a logarithm to turn the exponential series into the linear scale. But logarithmic is the wording that is commonly used. is not a logarithmic series, but an exponential one. Some readers will correct me by saying that 1 2 4 8. Take a look at the series above again: you can easily mark numbers down to a decimal precision on the beginning of the logarithmic scale (like 1.5 standing right in between 1 and 2) but won't care as much at the other end of the scale: if you were asked to position the numbers 800 and 853, you'd locate them somewhere between 5, without much of a difference between the two. That's exactly what the logarithmic scale does naturally for you. Another one: you don't need a quote down to the cent when buying a car, but a few cents may matter when buying a beer. However, the same amount of money would be perceived as marginal, if you were drawing a salary of six figures. A $1,000 salary increase is truly substantial. More than this, logarithmic is also the way we often do think, too. It allows us to discern subtleties around the smaller inputs, yet respond to the bigger inputs without being overrun. ![]() ![]() Look at the two scales above: by the same amount of space, the linear scale only goes up to 10, while the logarithmic extends to 1024! And remarkably, the logarithmic scale keeps the same precision as the linear scale around the small numbers. The linear scale is the most intuitive when it represents numbers along an axis the logarithmic one is much more powerful when one needs to work with a large dynamic range. On a logarithmic scale, numbers are evenly spaced, not by an additive factor - this is the linear scale - but a multiplicative factor. First, let's explain what the logarithmic scale is.
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