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the gain modification element unobtrusive to the listener. This is dependent on program material and not as easy as it sounds. Skilled audio engineers spend years finding out how to get these settings right for given program types. Digitization of audio presents the operator with several opportunities to severely degrade the audio signal inadvertently. In the first case, the operator must select an appropriate sample rate, so that sufficient bandwidth is allowed to capture all of the features of the audio required. Secondly, the quantization level must be chosen to represent the audio with minimal quantization noise. If the audio is digitized at a low sample rate, using only eight bits per sample, for example, the signal to noise ratio will be limited by the small number of bits used in the representation and the higher frequency components will be entirely absent due to the low sampling rate. If this audio track is subsequently encoded using one of the higher-quality encoding profiles (such as MP3, for example), the audio will still sound bad to the end user, simply because so much quality was thrown away at the digitization stage. In general, it is always better to digitize source material in the highest quality possible. Another nasty problem that can be introduced when audio is digitized is that if the sampling rate (the rate at which snapshots of the instantaneous audio waveform height are taken) is less than twice the highest frequency component present, aliasing noise can be introduced. This is because the digitization process is uncertain about how to represent frequencies higher than the Nyquist limit. In other words, given the limited number of data points output from the digitizer per unit time, a number of possible waveforms can be superimposed to satisfy those data points. The ear actually hears these other possible waveforms. Aliasing noise sounds like an in-band whining sound, which varies according to the highest frequency component present (its frequency is actually the difference between the sampling frequency and the high frequency component). For clean digitization at a certain sampling rate, it is necessary to first entirely remove frequency components above half the sampling rate. This is done with an anti-aliasing filter. In the past, these filters had to be of high-order, for good out-of-band rejection, but the higher the order, the more likely the filter was to overshoot and ring, introducing its own objectionable audio noises. These days, audio is typically oversampled at some multiple of the required sampling frequency, so that the analog filter used to remove out-of-band components can be designed using lower-order filters that introduce fewer artifacts of their own. Once digitized at several times the required sample rate, the data is reduced (decimated) using digital filtering techniques to eliminate the
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frequency components outside the required audio band. Hence, part of the anti-aliasing filtering takes place in the analog domain, prior to digitization and the remainder of the audio anti-aliasing takes place in the digital domain, after digitization. What this means to the streaming media compressionist is that they need to select digitization hardware that has been designed to eliminate quantization and aliasing noise. For cost reasons, some commonly available sound cards do a relatively poor job at reducing these digitization artifacts than costlier equipment designed for professional applications. Not all audio digitization devices are the same. Choose a good one. Unless you have been living in a cave for the last twenty years, you would have encountered graphic equalizers. These devices allow parts of the audio signal to be enhanced, while other things are de-emphasized. These are frequency-selective devices that can be used to make the audio signal sound subjectively better. Other types of equalizer are available for professional use, the most common of which is the four band parametric equalizer. Skilled compressionists can use equalization techniques to produce more acceptable-sounding audio streams, at least partly compensating for the losses introduced by the data compression process itself. Audio frequency equalization can also be used to reduce audible artifacts in low bit rate audio encoding. For example, there is a well-known warbling effect that happens on highly compressed, low bit rate audio streams, which has the effect of making the audio sound like it is being transmitted from the moon. The remedy is to roll off frequency components above 9 kHz. This roll-off could have been achieved during digitization, but a single audio digitization may need to be encoded at multiple bit rates, so for the lower bit rate encoding, further reductions in audio bandwidth must be made. Another frequency-selective correction that can be made is notch filtering. If the audio signal also includes a fixed frequency noise component, such as AC mains hum, for example, a notch filter can be used to reduce the apparent level of that frequency component. Today, much audio equalization and filtering is done after digitization, using digital filter implementations. These digital filters must be designed to use sufficient bit widths in their internal arithmetic to avoid introducing rounding errors, which sound subjectively like quantization noise. Digital filters can be a source of noise as well as a remedy for unwanted audio artifacts. An interesting variant of frequency-selective audio enhancement is the dynamic noise reduction process. This process makes the assumption
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