The spectrogram is one of the most illuminating and informative audio tools at our disposal. In this article, we’ll dive into how a spectrogram works, how to use one to examine an audio file, and how to fine-tune the type and amount of information presented in the RX spectrogram.
A spectrogram is a detailed view of audio, able to represent time, frequency, and amplitude all on one graph. A spectrogram can visually reveal broadband, electrical, or intermittent noise in audio, and can allow you to easily isolate those audio problems by sight. Because of its profound level of detail, a spectrogram is particularly useful in post production—so it’s not surprising that you’ll find one in tools like RX 7 and Insight 2.
In audio software, we’re accustomed to seeing a waveform that displays changes in a signal’s amplitude over time. A spectrogram, however, displays changes in the frequencies in a signal over time. Amplitude is then represented on a third dimension with variable brightness or color.
Let's take a look at an audio file in a traditional waveform view and a spectrogram. First, here’s a sine wave moving up in pitch from 60 Hz–12 kHz, as seen in a traditional waveform:
You’ll notice that the waveform shows amplitude over time, but we can’t really see what’s happening at individual frequencies. We can see that the sine wave is at a consistent level for the duration of the file, but we can’t tell much about how the pitch or frequency changes over time.
Here is the same audio file using a spectrogram.
In the spectrogram view, the vertical axis displays frequency in Hertz, the horizontal axis represents time (just like the waveform display), and amplitude is represented by brightness.
The black background is silence, while the bright orange curve is the sine wave moving up in pitch. This allows us to view a range of frequencies (lowest at the bottom of the display, highest at the top) and how loud events at different frequencies are. Loud events will appear bright and quiet events will appear dark.
Now, let’s look at a more complex audio example: the human voice.
Here’s a short, spoken phrase as seen through a waveform display. What we see here is the amplitude of the spoken words over time.
If we switch to the Spectrogram view, we’ll see many things we can’t see in the waveform view.
This is why having a detailed spectrogram display is so important in audio editing: it helps to clearly display the problems that you might want to fix.
The key to successful audio restoration lies in your ability to correctly analyze the situation—much like a doctor recognizing symptoms that point to a certain illness.
Constantly training your ear to distinguish the noises and audio events that need to be corrected can be a life-long endeavor. Fortunately, as explained previously, spectrogram technology makes this task easier by representing those audio events visually.
RX features an advanced spectrogram display that is capable of showing greater time and frequency resolution than other spectrograms, allowing you to see an unprecedented level of detail when working with audio.
An overview of the entire audio file's waveform will be displayed above the main Spectrogram/Waveform display in a Waveform Overview. The Waveform Overview will always display the entire audio file and will also display any selections made in the main display.
You can also view the traditional waveform, or a blend of both, by adjusting the Waveform/Spectrogram Opacity slider to the left just below the spectrogram.
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.
Not all spectrograms are created equal. An algorithm known as the “Fast Fourier Transform,” or FFT for short, is used to compute this visual display. Many plug-ins that feature a spectrogram display allow you to adjust the size of the FFT, but what does this mean for audio repair and restoration? Changing the FFT size will change the way the algorithm computes the spectrogram, causing it to look different. Depending on the type of audio you’re working with and visualizing, changing the FFT size may help.
As a rule, higher FFT sizes give you more detail in frequencies, referred to as frequency resolution, while lower FFT sizes give you more detail in time, referred to as time resolution.
If you’re trying to identify a plosive, mic handling noise, or other muddy low-frequency information, a higher FFT size in your spectrogram settings will help. If you’re trying to identify a high frequency event, or working with a transient signal (such as a percussion or drum loop), choose a lower FFT size.
There are a number of different audio problems that the tools in RX can help you fix. Identifying what kind of problem you have can help determine the most appropriate tool and method for treating the problem.
We’ve collected tips to help you identify seven common types of audio problems in a spectrogram, plus the modules in RX to remove them quickly and effectively. The audio problems we’ll be covering are:
Hum is usually the result of electrical noise somewhere in the recorded signal chain. It’s normally heard as a low-frequency tone at either 50 Hz or 60 Hz, depending whether you recorded audio in North America or Europe.
You’ll see hum by zooming in on the low frequencies. It’ll appear as a series of horizontal lines, usually with a bright line at 50 Hz or 60 Hz and several lighter lines at harmonics.
To remove hum, use the RX De-hum module. It works best when frequencies of the hum do not overlap with any useful transient signals.
In some cases, electrical noise will extend up to higher frequencies and manifest itself as a buzz. Sounds like these can also come from fluorescent light fixtures, motors, and some on-camera microphones.
You’ll find buzz in high frequencies, where it will appear as a thin horizontal line.
To remove buzz at frequencies above 400 Hz, use the Spectral De-noise tool. For low-frequency buzz, similar to hum, the De-hum tool is more effective.
Unlike hum and buzz, broadband noise is not concentrated at specific frequencies and can be found throughout the frequency spectrum. Tape hiss and noise from fans and HVAC systems are great examples.
In the spectrogram display, broadband noise usually appears as speckles that surround the program material, as seen in the example.
Use the Spectral De-noise tool to remove these types of broadband noise.
Clicks and pops are common on recordings made from vinyl, shellac, and other grooved media. They can also be introduced by digital errors, including recording into a DAW with too low of a buffer setting, or a bad audio edit that missed a zero crossing. Even mouth noises, such as tongue clicks and lip smacks, fall into this category.
You’ll see these short, impulsive noises appear in a spectrogram as vertical lines. The louder the click or pop, the brighter the line will appear. This example shows clicks and pops appearing in an audio recording transferred from vinyl.
For general clicks and pops, use the De-click module to recognize, isolate, reduce, and remove them. If you’re dealing with mouth clicks from a person speaking, the Mouth De-click module is the way to go.
Digital clipping is an all-too-common problem in audio production. It can occur when a signal is too loud to be recorded by an analog-to-digital converter, mixing console, field recorder, or some other gain stage in the signal chain. This can cause distortion, and the loss of audio information at the signal’s peaks.
To identify clipped audio, you’ll want to work with a waveform display, rather than a spectrogram. The clipping appears as “squared-off” sections of the waveform.
Zoom in on a waveform to see where the wave has been truncated because of clipping.
Note that sometimes, brickwall-limited audio will also appear “squared off,” 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 actually clipped.
To fix clipping, use the De-clip tool, which can intelligently redraw the waveform where it might have naturally been if the signal hadn’t clipped.
Intermittent noises are different from hiss and hum—they may appear infrequently and be inconsistent in pitch or duration. Common examples include coughs, sneezes, footsteps, car horns, ringing cell phones, birds, and sirens.
These noises can manifest themselves in various ways. Here are a couple examples:
Use the Spectral Repair tool to isolate these intermittent sounds, analyze the audio around them, and attenuate or replace them.
Sometimes a recording may have short sections of missing or corrupted audio. These are called gaps or dropouts.
These are usually very obvious to both the eye and the ear, and appear as a gap in the spectrogram display.
Use the Spectral Repair and Ambience Match tools to replace missing audio elements and create a consistent audio track.
The spectrogram has been a staple in RX since version one, and it’s not difficult to see why. By offering a surgically precise visualization of the audio you’re working on, in a way that allows you to understand more about the audio by sight, you can now identify areas that need correction with multiple senses. And when used in conjunction with the industry-leading audio repair modules in RX, it can help you make quick work of your next post production project. Happy editing!
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