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As with medical diagnostics, the key to successful audio restoration lies in your ability to correctly analyze the subject’s condition. This can be a life-long, never-ending quest, constantly honing the ear to distinguish the noises and audio events that need to be corrected.

To get started, it’s important to identify the problems with your file and identify which tool(s) will give you the results you want. Let’s briefly look at how to examine your audio using the spectrogram and waveform display tools, then consider how to identify audio problems using these displays.

What’s the goal of using a Spectrogram?

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RX 6 Overview. IZotope’s award-winning RX 6 Audio Editor is the industry standard for audio repair, restoration, and enhancement. It offers a comprehensive suite of tools focused on alleviating common to complex audio issues. Oct 12, 2012 50+ videos Play all Mix - Repair Clipped Audio with RX 2 iZotope Tips From A Pro YouTube HOW TO EQ VOCALS - Simple 3 Step Formula For Eqing Vocals - Duration: 14:15. Rob Williams 1,767,199 views.

2) Click the Start button in the 'Auto-Normalize' control panel. The Start button will turn into a Stop button. 3) When you've played through the entire mix, or at least the point that has the highest level, click Stop. 4) Adjust the slider to the level you want your mix to peak at (e.g. Note that you can specify the level in percent or dB. Jul 06, 2017 In this video, we show you how to remove harsh clipping and background noise on a podcast narrator’s vocal track with RX Elements. Quickly repair clipped audio with RX Elements De-clip module, a. IZotope’s RX 6 is here and so is master trainer Joe Albano with an in-depth look at every module included in the RX Advanced collection. Join him in this course, and learn how to repair, enhance, and restore damaged audio you thought was unrecoverable. Identifying Audio Problems. As with medical diagnostics, the key to successful audio restoration lies in your ability to correctly analyze the subject’s condition. This can be a life-long, never-ending quest, constantly honing the ear to distinguish the noises and audio events that need to be corrected.

The aim of any good visualization tool for audio repair and restoration is to provide you with more information about an audible problem. This not only helps inform your editing decisions, but, in the case of a spectrogram display, can provide new, exciting ways to edit audio, especially when used in tandem with a waveform display.

Hum

Hum is usually the result of electrical noise somewhere in the recorded signal chain. It’s normally heard as a low-frequency tone based at either 50 Hz or 60 Hz depending on where the recording was made If you zoom in to the low frequencies, you’ll be able to see hum as a series of horizontal lines, usually with a bright line at 50 Hz or 60 Hz and several less intense lines above it at harmonics. See the example below:

De-hum works best when frequencies of the hum do not overlap with any useful transient signals. You can learn more about the De-hum tool here.

Buzz

In some cases, electrical noise will extend up to higher frequencies and manifest itself as a background buzz. See the example below:

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Hum-removal tools usually focus on low-frequency hum, so when the harmonics extend to frequencies above 400 Hz, the Spectral De-noise tool is often more effective at removing the problem.

Hiss and other Broadband Noise

Unlike hum and buzz, broadband noise is spread throughout the frequency spectrum and isn’t concentrated at specific frequencies. Tape hiss and noise from fans and air conditioners are good examples of broadband noise. In a spectrogram display, broadband noise usually appears as speckles that surround the program material. See the example below: 

Clicks, Pops, & Short Impulse Noises

Clicks and pops are common on recordings made from vinyl, shellac and other grooved media, but can also be introduced by digital errors, including recording into a DAW with improper buffer settings, or making a bad audio edit that missed a zero crossing. Even mouth noises such as tongue clicks and lip smacks fall into the clicks category. These short impulse noises appear in a spectrogram as vertical lines. The louder the click or pop, the brighter the line will appear. The example below shows clicks and pops appearing in an audio recording transferred from vinyl: 

The De-click tool can recognize, isolate, and then reduce and remove clicks like these.

Clipping

Clipping is an all-too-common problem. It can occur when a loud signal distorts the input to an audio interface, analog-to-digital converter, mixing console, field recorder, or other sound capture device. A spectrogram is not particularly useful for identifying clipped audio—for this you’ll want to work with a waveform display. As you’ll see in the image below, the clipping appears as “squared-off” sections of the waveform.

You can zoom in on a waveform and see in detail where the waveform has been truncated because of clipping.

The De-clip tool can intelligently redraw the waveform to where it might have naturally been if the signal hadn’t clipped. Sometimes, brickwall limited audio will also appear “squared off” when zoomed out, but this doesn’t necessarily mean it will sound as heavily distorted as clipped waveforms that have been truncated. You can zoom in to see if the tops of individual waveforms are clipped.

Intermittent Noises

Intermittent noises are different than hiss and hum—they may appear infrequently and may not be consistent in pitch or duration. Common examples include coughs, sneezes, footsteps, car horns, ringing cell phones, etc. The images below represent two different examples of these noises:

The Spectral Repair tool can help isolate these intermittent sounds, analyze the audio around them and attenuate or replace them.

Gaps and Drop Outs

Sometimes a recording may have short sections of missing or corrupted audio. These are usually very obvious to both the eye and the ear! See the example below:

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Deleting the gap and then applying Spectral Repair to replace any missing audio can help fix these problems.

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Lossy encoding can be viewed as a low-bit-depth quantization of a signal. The precision of this quantization depends on the selected bitrate, while quantization noise (a compression error: the difference between the original and the decoded signal) is spectrally shaped to be minimally audible — this is achieved by a psychoacoustic model.

The amplitude of quantization noise depends on the chosen bitrate and signal complexity. Slowly-changing tonal signals are easy to approximate, while random noises are hard (see the examples below). The amplitude of compression noise is often proportional to the signal level, much like with a 32-bit floating-point sample format. The noise of a 32-bit float format is always 150 dB lower than the signal level, while the noise of mp3 or AAC compression is usually only 15–30 dB below the signal level.

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When quantization noise is added to the waveform, it can change the peaks levels. If the waveform has been brickwall-limited to a certain level, chances are that 50% of waveform peaks will rise in level.

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