IZotope RX Advanced Audio Editor is the most complete audio recovery product on the market, a unique standalone application designed from the ground up to combat a range of audio problems. In addition to unique workflow features designed to help you get the best results, RX’s powerful tools can do processing that plug-in-based restoration products simply can not do. Sep 25, 2014  Fortunately, spectrogram technology—which is included in our award-winning audio repair toolkit, RX—makes this task easier by providing a visual representation of audio. The topics covered in this blog post include: How a spectrogram works; How.

1. Izotope Rx Spectrogram 2017
2. Izotope Rx Torrent
3. Izotope Rx 7 Torrent Windows
4. Izotope Rx Reviews
5. Izotope Rx Download

Figure 1: The stand-alone version of iZotope RX 2 Advanced. The right side of the GUI provides access to processing modules. Clicks in the audio file are displayed as three vertical lines in the center of the golden spectrogram, in which the floating waveform overlay is disabled.

iZotope’s critically acclaimed RX 1 restoration software offered impressive processing for removing clicks, hum, broadband noise and even isolated events such as chair squeaks from audio files. Two updated versions of the software, RX 2 and the more-powerful RX 2 Advanced, add a multitude of features for even more effective restoration and easier archiving.

The software comes in both stand-alone and plug-in versions. The stand-alone version includes a world-class spectrogram with floating waveform overlays and several processing modules that can be used either independently or in combination (see Fig. 1). The Declipper, Declicker & Decrackler, Hum Remover, Denoiser and Spectral Repair processing modules included in the stand-alone version can also be instantiated as separate DAW plug-ins in RTAS, AudioSuite, VST, MAS, AU and DirectX formats. (The Spectral Repair plug-in includes essentially the same spectrogram as the stand-alone software; the plug-in provides offline processing and is not compatible with some hosts.) The stand-alone versions of RX 2 and RX 2 Advanced also offer a 6-band equalizer, gain adjustments (including four types of fades), L/R channel-balancing controls (including phase rotation) and a real-time spectrum analyzer.

NEW TOOLS
Why upgrade from RX 1? RX 2 includes new freehand (paint-brush and lasso) and automatic (Magic Wand) tools for selecting unwanted events (clicks, dog barks, fret buzz and so on) for attenuation or removal in its spectrogram. You can adjust pre- and post-roll around selections in the spectrogram. The new Decrackler is indispensable for restoring vinyl recordings. The Hum Remover can automatically find the base frequency in need of processing, and the Denoiser and Declicker algorithms have been improved since RX 1’s release. You can chain multiple processing modules together when batch-processing files. Your edit history is automatically saved when you quit, and it’s restored after a crash.

RX 2 Advanced includes all of RX 2’s features and more. An adaptive Denoiser mode removes background noise that changes over time, a lifesaver for video post. A new Deconstruct module lets you accentuate or attenuate noisy and pitched components of sound independently. (Imagine making a vocal track sound more breathy.) RX 2 Advanced features third-party plug-in hosting (one AU, VST or DirectX plug-in at a time) for processing spectrogram selections and batch processing. Other features include proprietary 64-bit SRC sample-rate conversion, MBIT+ dithering, Radius pitch-shifting and time-stretching, automatic azimuth re-alignment (for tape restoration) and a time-stamped log (invaluable for forensics and archival work).

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The SRC, Time/Pitch and Spectral Repair processors work only offline in both RX 2 and RX 2 Advanced. All the other modules work in real time. I reviewed RX 2 Advanced in stand-alone mode and its plug-in versions in Digital Performer Version 7.21 (DP) using an 8-core Mac Pro running OS X 10.5.8.

Figure 2: The Denoiser plug-in allows independent attenuation of tonal and broadband noise components.

CLEAN-UP TIME
RX 2 Advanced proved invaluable while re-recording (engineering the final mix for) an independent documentary video. During one outdoor scene, the Denoiser greatly tamed broadband noise from distant traffic that all but buried very quiet dialog. I could achieve more than 4 dB of noise reduction without producing any watery artifacts or dulling high frequencies. In another scene, the Declipper completely rebuilt squared-off waveforms and purged distortion on a dialog track that had been recorded too hot and had clipped hard. Amazing.

The Hum Remover includes a mode that supposedly lets you hear only the hum component of an audio file for easier adjustment of control settings, but I heard total silence instead. While moving some of the plug-in’s controls, audio is momentarily passed through unprocessed, which is a distraction when fine-tuning settings. Despite these annoyances, the Hum Remover was phenomenally effective in removing 60Hz hum and its first and second harmonics from a dialog track recorded with a poorly grounded lavalier mic. I could completely remove the hum without significantly changing the track’s timbre. And on a Foley track, Free mode let me manually dial in the corrective frequency and Q settings to eliminate a 153Hz hum of unknown origin.

On a voice-over track, Declicker seamlessly removed lip smacks while only very slightly reducing depth. And the Denoiser transparently reduced broadband noise around 9 dB on a noisy music track (see Fig. 2).

THE MIRACLE WORKER
Spectral Repair can remove sounds that no other processor can touch. It uses interpolation of surrounding material to seamlessly fill in the resulting holes. I felt most comfortable using the plug-in version of Spectral Repair. As the audio files for the video didn’t include timecode metadata, exporting to the stand-alone version would have made subsequent lock-to-picture of the repaired files prone to error after importing them back into DP. That said, the interfaces for the stand-alone and plug-in versions need improvement.

The plug-in version offers real-time previewing (allowing you to hear the results of your control settings before rendering), while the stand-alone version currently doesn’t and requires a workaround. With the stand-alone version, you can undo, change your settings and process your selections again to hear the result of different settings. Alternatively, you can use the included Compare function to cache the effect of different settings for comparison purposes before committing permanently; after the caching is completed, you initiate playback to hear the cached results in turn. Neither the Undo nor Compare workarounds allow pre- and post-roll playback around the events you wish to process (the plug-in allows this), forcing you to evaluate the processing of split-second-duration events in isolation from the surrounding material you wish to preserve.

Despite its shortcomings, the stand-alone version of Spectral Repair offers a few advantages over the plug-in. Its spectrogram has a scrolling playback wiper that makes it much easier to identify the exact location of noises you want to eliminate. (iZotope hopes to include this feature in the plug-in in a future release.) The stand-alone version can also automatically find similar events to the one you’ve currently selected; this makes it much easier to, for example, remove several chair squeaks in turn without having to separately search for and manually select each one for processing.

I got my best results using the Lasso tool to select multiple objects—by drawing a border around them with my mouse while holding the Shift key—to process in the spectrogram. To my amazement, I was able to completely remove three very closely spaced and loud broadband clicks embedded in a music production’s final mix without inflicting any audible penalty whatsoever on the desired material. (The frequency bandwidth of the clicks was too wide for the Declicker to be effective.) Like removing sugar from a cake after it had already been mixed and baked, Spectral Repair did the impossible.

MAYBE I’M AMAZED
Most of the modules for RX 2 Advanced work brilliantly. The Hum Remover is a little buggy but nevertheless yields terrific results. The user interface for Spectral Repair needs improvement but won’t stop you from attenuating or removing seemingly intractable noises. The learning curve is a little steep due to a somewhat poorly written and insufficient operating manual.

Few products astonish this seen-it-all engineer. RX 2 Advanced—especially Spectral Repair—floored me. RX 2 Advanced offers a world-class toolset that’s indispensable for anyone involved in audio restoration and archiving, forensics, post-production, music mastering and cleaning up noise-riddled tracks recorded in poorly isolated home studios.

Mix

contributing editor Michael Cooper is the owner of Michael Cooper Recording in Sisters, Ore.

Click on the Product Summary box above to view the RX 2 Advanced product page.

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.

Izotope Rx Spectrogram 2017

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.wav

We 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.wav

We 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.

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:

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.wav

After

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https://izotopetech.files.wordpress.com/2017/03/de-rustle_proc.wav

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Before

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https://izotopetech.files.wordpress.com/2017/03/de-rustle-3.wav

After

https://izotopetech.files.wordpress.com/2017/03/de-rustle-3_proc.wav

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Conclusion

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.