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Module & Plug-in |
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Overview
De-clip repairs digital and analog clipping artifacts that result when A/D converters are pushed too hard or magnetic tape is over-saturated. It can be extremely useful for rescuing recordings that were made in a single pass, such as live concerts or interviews, momentary clipping in “perfect takes”, and any other audio that cannot be re-recorded.
In the RX Audio Editor, the Histogram meter updates based on selection: Select a section of the recording where clipping is prominent and De-clip will analyze the levels of the program material. If clipping is present in the selection, it will usually appear as a horizontal line in the histogram that extends all the way across the meter. Izotope are the latest developers to release a suite of tools for tackling problem audio. As well as the usual noise-reduction algorithms, RX also includes clip reduction and an. New 'Prevent Clipping' export option: Predicts and prevents clipping when exporting to OGG & MP3 file formats in the RX Audio Editor. Mouth De-click Processing Improvements: Improved Mouth De-click results for Japanese dialogue. Improved processing time when using RX Connect for Direct Offline Processing in Nuendo.
De-clip will process any audio above a given threshold, interpolating the waveform to be more round. Generally, the process is as easy as finding the clipping you want to repair, then setting the threshold just under the level where the signal clips.
Controls
- HISTOGRAM METER: Displays waveform levels for the current selection as a histogram. The histogram meter helps you set the Threshold control by displaying the audio level where the waveform’s peaks are concentrated. This usually indicates at what level clipping is present in the file. The longer the line for the histogram is, the more energy is present at that amplitude.
- HISTOGRAM ZOOM CONTROLS: The histogram’s range can be scaled if you need a better view of your signal. Use the (+) and (-) buttons to scale your display and value resolution for the De-clip module. These buttons reduce (+) and/or expand (-) the range of the threshold slider and histogram. Djay pro 10 17 1. You may want to extend the histogram range when the clipping point is lower than what you can see on a histogram or if you don’t see anything on the histogram.Note on histogram updating in the application vs. the De-clip plug-in
- In the RX Audio Editor, the Histogram meter updates based on selection: Select a section of the recording where clipping is prominent and De-clip will analyze the levels of the program material. If clipping is present in the selection, it will usually appear as a horizontal line in the histogram that extends all the way across the meter.
- In the De-clip Plug-in, the histogram runs as a real-time meter.
What is a histogram?- A histogram is an analytical tool that displays how many samples are present at a given signal level over a window in time. The longer the line for the histogram is, the more energy is present at that amplitude.
- If a lot of energy tends to collect near the top and bottom edges of a waveform, that waveform is probably clipped and distorted.
- In the RX Audio Editor, the Histogram meter updates based on selection: Select a section of the recording where clipping is prominent and De-clip will analyze the levels of the program material. If clipping is present in the selection, it will usually appear as a horizontal line in the histogram that extends all the way across the meter.
- THRESHOLD [dB]: Defines the level used for detection of clipped intervals. Generally, this should be set just below the actual level of clipping. To set the threshold, move the Threshold slider until it lines up with the place in the histogram just below where clipping is concentrated.Understanding the Clipping Threshold overlays
- Adjusting the Clipping Threshold will display a blue line within the histogram and a gray line on the waveform itself (when the De-clip Threshold effect overlay). These lines indicate the audio information that will be considered as “clipping” by the de-clip algorithm.
Using the De-clip Threshold Effect overlay in the Spectrogram/waveform view- By default, De-clip Threshold is enabled in the View > Effect Overlay menu.
- When enabled and the De-clip module is open, the De-clip threshold overly will be displayed in the spectrogram/waveform display.
- You can adjust the Threshold controls in the De-clip module by adjusting the Threshold overlay lines in the spectrogram display.
- You can use the mousewheel on the waveform amplitude ruler to adjust the threshold control values.
- Adjusting the Clipping Threshold will display a blue line within the histogram and a gray line on the waveform itself (when the De-clip Threshold effect overlay). These lines indicate the audio information that will be considered as “clipping” by the de-clip algorithm.
- THRESHOLD LINK: Toggles the ability to adjust positive and negative clipping thresholds independently.
- When this option is enabled, you can adjust the positive and negative clipping Threshold controls independently. This is useful in cases where more clipping is occurring on one side of your waveform.
- You can also set asymmetric de-clipping thresholds directly from the waveform by toggling the lock box between the threshold controls on the waveform display.
- When this option is enabled, you can adjust the positive and negative clipping Threshold controls independently. This is useful in cases where more clipping is occurring on one side of your waveform.
- SUGGEST: Calculates suggested threshold values based on the levels in your current selection.
- QUALITY: Controls the interpolation processing quality. There are three quality modes in the De-clip module: Low, Medium, and High.De-clip Quality mode notes
- Low quality mode processes very quickly.
- High quality mode processes slowly but is capable of achieving better results.
- In many cases you will find that Low quality mode gives you great results. To save time, always start by previewing the Low quality modes first. You can also use the Compare feature to try multiple modes and preview the results.
- Low quality mode processes very quickly.
- MAKEUP GAIN [dB]: Selects the gain to be applied to the selection after De-clip.When to use the make-up gain control
- The De-clip process causes an increase in peak levels. The Makeup gain control can be used to prevent the signal from clipping after processing. It is also useful for matching the level after processing to unprocessed audio outside of the selection.
- The De-clip process causes an increase in peak levels. The Makeup gain control can be used to prevent the signal from clipping after processing. It is also useful for matching the level after processing to unprocessed audio outside of the selection.
- POST-LIMITER: Applies a true peak limiter after processing to prevent the processed signal from exceeding 0 dBFS.
- De-clip usually increases signal levels by interpolating signal segments “above” the clipping point, which can make the signal clip again if the waveform format offers no headroom above 0 dBFS.
- If the post-limiter is disabled, the restored intervals above 0 dBFS can be safely stored even without makeup gain as long as the file is saved as 32-bit float. Intervals above 0 dBFS will clip when played back through a digital-analog converter.
- De-clip usually increases signal levels by interpolating signal segments “above” the clipping point, which can make the signal clip again if the waveform format offers no headroom above 0 dBFS.
More Information
Suggestions for severe distortion
- For certain situations, using the Deconstruct module to extract the noise components of the distortion can help remove additional artifacts beyond the clipped peaks in a waveform.
- In cases where severe distortion is visible on the spectrogram, the Spectral Repair tool can be used to select those problem areas, and attenuate or replace them with undistorted audio.
Visual Examples
BEFORE & AFTER CLIP REPAIR
A waveform before and after clip repair. The after example (bottom) shows the original repaired waveform (faded) and the post-limiting waveform (bright).
UNLINKING THRESHOLD CONTROLS TO CURB ASYMMETRIC CLIPPING
This problematic waveform (grey) shears off around −13 dB on only the positive side of the waveform (the histogram on the right shows the extra positive energy of clipped audio). Extra processing of the negative side would be unnecessary, so the Threshold controls can be unlinked to process above −13 dBFS on the positive side only. The resulting waveform is drawn in blue above the grey sheared peak.
By inconspicuously attaching on clothing near a person’s mouth, the lavalier microphone (lav mic) provides multiple benefits when capturing dialogue. For video applications, there is no microphone distracting viewer attention, and the orator can move freely and naturally since they aren’t holding a microphone. Lav mics also benefit audio quality, since they are attached near the mouth they eliminate noise and reverberation from the recording environment.
Unfortunately, the freedom lav mics provide an orator to move around can also be a detriment to the audio engineer, as the mic can rub against clothing or bounce around creating disturbances often described as rustle. Here are some examples of lav-mic recordings where the person moved just a bit too much:
https://izotopetech.files.wordpress.com/2017/03/de-rustle-3.wavhttps://izotopetech.files.wordpress.com/2017/03/de-rustle.wav
Rustle cannot be easily removed using the existing De-noise technology found in an audio repair program such as iZotope RX, because rustle changes over time in unpredictable ways based on how the person wearing the microphone moves their body. The material the clothing is made of also can have an impact on the rustle’s sonic quality, and if you have the choice attaching it to natural fibers such as cotton or wool is preferred to synthetics or silk in terms of rustling intensity. Attaching the lav mic with tape instead of using a clip can also change the amount and sound of rustle.
Because of all these variations, rustle presents itself sonically in many different ways from high frequency “crackling” sounds to low frequency “thuds” or bumps. Additionally, rustle often overlaps with speech and is not well localized in time like a click or in frequency like electrical hum. These difficulties made it nearly impossible to develop an effective deRustle algorithm using traditional signal processing approaches. Fortunately, with recent breakthroughs in source separation and deep learning removing lav rustle with minimal artifacts is now possible.
Audio Source Separation
Often referred to as “unmixing”, source separation algorithms attempt to recover the individual signals composing a mix, e.g., separating the vocals and acoustic guitar from your favorite folk track. While source separation has applications ranging from neuroscience to chemical analysis, its most popular application is in audio, where it drew inspiration from the cocktail party effect in the human brain, which is what allows you to hear a single voice in a crowded room, or focus on a single instrument in an ensemble.
We can view removing lav mic rustle from dialogue recordings as a source separation problem with two sources: rustle and dialogue. Audio source separation algorithms typically operate in the frequency domain, where we separate sources by assigning each frequency component to the source that generated it. This process of assigning frequency components to sources is called spectral masking, and the mask for each separated source is a number between zero and one at each frequency. When each frequency component can belong to only one source, we call this a binary mask since all masks contain only ones and zeros. Alternatively, a ratio mask represents the percentage of each source in each time-frequency bin. Ratio masks can give better results, but are more difficult to estimate.
For example, a ratio mask for a frame of speech in rustle noise will have values close to one near the fundamental frequency and its harmonics, but smaller values in low-frequencies not associated with harmonics and in high frequencies where rustle noise dominates.
To recover the separated speech from the mask, we multiply the mask in each frame by the noisy magnitude spectrum, and then do an inverse Fourier transform to obtain the separated speech waveform.
Mask Estimation with Deep Learning
The real challenge in mask-based source separation is estimating the spectral mask. Because of the wide variety and unpredictable nature of lav mic rustle, we cannot use pre-defined rules (e.g., filter low frequencies) to estimate the spectral masks needed to separate rustle from dialogue. Fortunately, recent breakthroughs in deep learning have led to great improvements in our ability to estimate spectral masks from noisy audio (e.g., this interesting article related to hearing aids). In our case, we use deep learning to estimate a neural network that maps speech corrupted with with rustle noise (input) to separated speech and rustle (output).
Since we are working with audio we use recurrent neural networks, which are better at modeling sequences than feed-forward neural networks (the models typically used for processing images), and store a hidden state between time steps that can remember previous inputs when making predictions. We can think of our input sequence as a spectrogram, obtained by taking the Fourier transform of short-overlapping windows of audio, and we input them to our neural network one column at a time. We learn to estimate a spectral mask for separating dialogue from lav mic rustle by starting with a spectrogram containing only clean speech.
https://izotopetech.files.wordpress.com/2017/04/clean_speech.wavWe can then mix in some isolated rustle noise, to create a nosiy spectrogram where the true separated sources are known.
https://izotopetech.files.wordpress.com/2017/04/noisy_speech.wavWe then feed this noisy spectrogram to the neural network which outputs a ratio mask. By multiplying the ratio mask with the noisy input spectrogram we have an estimate of our clean speech spectrogram. We can then compare this estimated clean speech spectrogram with the original clean speech, and obtain an error signal which can be backpropagated through the neural network to update the weights. We can then repeat this process over and over again with different clean speech and isolated rustle spectrograms. Once training is complete we can feed a noisy spectrogram to our network and obtain clean speech.
Gathering Training Data
We ultimately want to use our trained network to generalize across any rustle corrupted dialogue an audio engineer may capture when working with a lav mic. To achieve this we need to make sure our network sees as many different rustle/dialogue mixtures as possible. Obtaining lots of clean speech samples is relatively easy; there are lots of datasets developed for speech recognition in addition to audio recorded for podcasts, video tutorials, etc. However, obtaining isolated rustle noises is much more difficult. Engineers go to great lengths to minimize rustle and recordings of rustle typically are heavily overlapped with speech. As a proof of concept, we used recordings of clothing or card shuffling from sound effects libraries as a substitute for isolated rustle.
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https://izotopetech.files.wordpress.com/2017/04/cards_playing_cards_deal02_stereo.wav
These gave us promising initial results for rustle removal, but only worked well for rustle where the mic rubbed heavily over clothing. To build a general deRustle algorithm, we were going to have to record our own collection of isolated rustle.
We started by calling into the post production industry to obtain as many rustle corrupted dialogue samples as possible. This gave us an idea of the different qualities of rustle we would need to emulate in our dataset. Our sound design team then worked with different clothing materials, lav mounting techniques (taping and clipping), and motions from regular speech gestures to jumping and stretching to collect our isolated rustle dataset. Additionally, in machine learning any patterns can potentially be picked up by the algorithm, so we also varied things like microphone type and recording environment to make sure our algorithm didn’t specialize to a specific microphone frequency response for example. Here’s a greatest hits collection of some of the isolated rustle we used to train our algorithm:
https://izotopetech.files.wordpress.com/2017/04/rustle_training.wav
Debugging the Data
One challenge with machine learning is when things go wrong it’s often not clear what the root cause of the problem was. Your training algorithm can compile, converge, and appear to generalize well, but still behave strangely in the wild. For example, our first attempt at training a deRustle algorithm always output clean speech with almost no energy above 10 kHz, even though there was speech energy at those frequencies.
It turned out that a large percentage of our clean speech was recorded with a microphone that attenuated high frequencies. Here’s an example problematic clean speech spectrogram with almost no high-frequency energy:
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Since all of our rustle recordings had high frequency energy the algorithm learned to assign no high frequency energy to speech. Adding more high quality clean speech to our training set corrected this problem.
Before and After Examples
Once we got the problems with our data straightened out and trained the network for a couple days on a NVIDIA K80 GPU, we were ready to try it out removing rustle from some pretty messy real-world examples:
Before
https://izotopetech.files.wordpress.com/2017/03/de-rustle.wavAfter
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https://izotopetech.files.wordpress.com/2017/03/de-rustle_proc.wavBefore
https://izotopetech.files.wordpress.com/2017/03/de-rustle-3.wavAfter
https://izotopetech.files.wordpress.com/2017/03/de-rustle-3_proc.wavRemoving Clipping Izotope Rx 7
Conclusion
Removing Clipping Izotope Rx 1
While lav mics are an extremely valuable tool, if they move a bit too much the rustle they produce can drive you crazy. Fortunately, by leveraging advances in deep learning we were able to develop a tool to accurately remove this disturbance. If you’re interested in trying this deRustle algorithm give the RX 6 Advanced demo a try.