Introduction
When you’re recording music or mixing audio, unwanted background noise can be a persistent headache. The hum of an air conditioner, street sounds bleeding into a vocal track, or the buzz from an amp – these noises can spoil an otherwise great take. Traditionally, engineers tackled noise with EQ cuts, gates, or post-production noise reduction plugins. But these methods have limitations, especially with complex or changing noise. Enter AI-powered noise suppression – new tools that “learn” to separate voice or instrument sounds from noise. These tools can filter out everything from static hiss to barking dogs in real-time, almost like magic. Importantly, many powerful noise suppressors are now open-source, meaning they’re free and community-developed. In this article, we’ll explore the latest open-source AI noise suppression technologies (like RNNoise, DeepFilterNet, and GT-CRN) and see how they compare to proprietary options such as Krisp and NVIDIA RTX Voice. We’ll focus on practical aspects – audio quality, latency, CPU/GPU performance, and how easily you can integrate these tools into your DAW or live rig – all in a friendly, non-too-technical tone for musicians and audio pros.

The Rise of AI Noise Suppression

Traditional noise reduction struggled with anything beyond constant, low-frequency noise (think steady hums or fan noise). They used fixed algorithms or hardware solutions that weren’t adaptable. AI noise suppression changed the game by learning what noise sounds like. Instead of a one-size-fits-all filter, AI models analyze audio in real-time and distinguish between the “signal” (music or speech you want) and the “noise” (unwanted sounds). By training on large datasets of noisy and clean audio, these models continuously improve and can handle a variety of noise types. In practice, this means an AI noise canceller can mute a barking dog or a crowd chatter during a vocal performance, where old methods would falter. Open-source development has accelerated this field – the community is collaborating to push the tech forward, making high-quality noise suppression accessible to everyone. Before diving into specific tools, remember that any noise suppression can introduce some artifacts. The goal is to improve overall audio quality – greatly reduce noise without degrading the music or voice. As we’ll see, the best AI models today do an impressive job, but it’s good to use them judiciously (extreme settings can sometimes make voices sound a bit thin or “processed”). Now, let’s look at the notable open-source contenders.

Open-Source Noise Suppression Tools

Open-source projects are leading the charge in AI noise reduction. They allow anyone to inspect the code, contribute improvements, and even tailor the algorithms for specific needs. Here are some of the latest and most notable tools:

RNNoise – Lightweight Pioneer

RNNoise is the OG of open-source AI noise suppression. Launched by Mozilla about six years ago, it was groundbreaking at the time for blending classic DSP (digital signal processing) techniques with a trained neural network. This hybrid approach made RNNoise efficient enough to run in real-time on ordinary CPUs – perfect for live streaming or recording on a laptop. RNNoise essentially learned to recognize human voice characteristics and filter out everything else. In practice, it works well on constant noises like fan hum or air conditioner buzz. Users often praise how it “completely got rid of my fan sound”, allowing them to record in non-ideal environments. However, RNNoise is a fairly aggressive suppressor, and it can sometimes make voices sound slightly robotic or muffled when pushing it hard. As one audio engineer noted, it’s “small and fast, but not very accurate and has many edge cases where it fails”. In other words, extremely complex or non-stop noises may trip it up, and you might hear artifacts on the voice. RNNoise hasn’t seen significant updates in recent years (Mozilla no longer maintains it), and it’s starting to show its age. Despite that, it remains wildly popular because of its simplicity and low CPU use. It’s integrated as the “Noise Suppression” filter in OBS Studio and available via community plugins (for example, the werman/noise-suppression-for-voice plugin brings RNNoise to various systems). It’s basically plug-and-play: enable it on your live mic input and it’ll continuously clean the signal. Latency is negligible – RNNoise processes audio in short frames (~10-20ms), so you won’t notice delay. For many musicians and streamers on a budget, RNNoise has been a lifesaver to tame background noise. Just don’t expect miracles beyond moderate noise levels, and consider pairing it with a noise gate. (A gate can mute the mic when you’re not speaking or playing, while RNNoise reduces noise during speech – together they can be very effective.)

DeepFilterNet – High Quality Deep Learning

If RNNoise is the old guard, DeepFilterNet is the newer generation showing what open-source can do. Developed by researchers at University of Erlangen-Nuremberg, DeepFilterNet is a two-stage deep learning model designed specifically for real-time speech enhancement. Without diving too deep, it uses a clever two-step approach: first it applies a coarse filter on an audio’s spectral envelope (essentially shaping the tone to reduce noise), then a second stage deep filteringnetwork removes residual noise and enhances the speech harmonics. The result is impressively clean voice output with minimal warbling or tinny artifacts. Community members who have tried DeepFilterNet report it does an “absurdly good job of removing not-speech from inputs in realtime”, even in tough cases like vacuum cleaner noise or music playing in the background. In fact, its demo showcases scenarios like a guitar playing while someone speaks – DeepFilterNet can strip out the guitar and leave the voice, which is almost like a rudimentary source separator. Compared to RNNoise, it’s much better at preserving voice quality under heavy noise. One catch: the model is larger and more complex (on the order of a couple million parameters, vs RNNoise’s 60k), so it uses more CPU. It’s still optimized for real-time – one user integrated it into a Linux PipeWire setup and found it worked great for voice calls and gaming, though “it does take some technical elbow grease to integrate” at the moment. There isn’t an official VST/AU plugin widely available yet, so using DeepFilterNet might involve running a separate app or script that processes your audio (for instance, Linux users can use it via EasyEffects or as a PipeWire filter). Latency remains low (it’s designed for low-latency operation similar to RNNoise), but CPU usage will be higher. If you have a modern PC, you can likely run it alongside your DAW without issues, but on very low-power machines it might be taxing. The DeepFilterNet project released a second version (DeepFilterNet2) and even a third, focusing on efficiency improvements for embedded devices. This shows active development – the open-source community is iterating quickly. For audio engineers, DeepFilterNet is exciting because it brings studio-grade noise reduction quality without a price tag. Imagine cleaning up a vocal track that had city noise in the background – you could run it through DeepFilterNet and get a result approaching what expensive tools like iZotope RX might achieve, but in real-time. As of now, using it might require some tech tinkering, but the results speak for themselves.

GT-CRN – Ultra-Lightweight Next-Gen Suppressor

One of the most promising newcomers is GT-CRN, short for Grouped Temporal Convolutional Recurrent Network. Unveiled in late 2023/early 2024 by academic researchers, GT-CRN aims to deliver top-tier noise suppression at a fraction of the computational cost. How small is it? The model is only about 48k parameters, roughly the same size as RNNoise (which is ~60k), and it runs in real-time using only ~0.03 GFLOPs (that’s extremely low). Despite its tiny footprint, GT-CRN’s developers report that it “surpasses RNNoise” in noise reduction performance and even holds its own against recent, much larger models. In their tests, GT-CRN achieved speech quality scores (PESQ, STOI, etc.) comparable to bigger networks like DeepFilterNet and others, while using 10–50x less processing power. In plain terms: it gives you near-state-of-the-art noise cleaning without needing a beefy CPU or GPU. This is great news if you’re working on a modest laptop or want to implement noise suppression on a mobile device or pedalboard-style hardware. GT-CRN uses a mix of convolutional and recurrent layers (hence “CRN”) with some grouping tricks to keep it efficient. For musicians and mixing engineers, what matters is that it’s fast and sounds good. Early listeners say it introduces very little artifacting even in fairly noisy cases – likely a big improvement over RNNoise’s occasional robotic sound. The project’s repository even provides a live demo and notes a real-time factor of 0.07 on a midrange Intel i5 CPU, meaning it processes audio ~14 times faster than real time on that chip – blazingly fast. Being so new, GT-CRN isn’t yet packaged in common audio software, but you can experiment with it via its open-source code (the model and examples are on GitHub). We anticipate it won’t be long before this technology finds its way into OBS, VoIP apps, or plugins, given its efficiency. GT-CRN exemplifies how quickly open research is evolving: you get better noise suppression than before, with less CPU hit. It’s definitely a project to keep on your radar.

Other Notable Open Projects (DTLN, Facebook, etc.)

Beyond the big names above, there are other open-source noise suppression efforts worth mentioning:

  • DTLN (Dual-Signal Transformation LSTM) – This is a deep learning approach that processes audio in two stages (often one neural network handles the magnitude of the sound spectrum, another handles the phase). The original DTLN research came out of a university in 2020, and it was notable for being efficient enough for real-time use on a CPU. Recently, engineers at Datadog open-sourced dtln-rs, a noise suppression library in Rust built on the DTLN model. It’s designed to drop into WebRTC and other apps easily. On a standard M1 MacBook Pro, dtln-rs can “process one second of audio in just 33 ms”, well under real-time. In quality, DTLN-based filters are quite effective – the Datadog team recounts how their prototype completely removed a loud lawn mower sound from a live call, to the amazement of their colleagues. That’s a good sign that this tech can handle non-stationary noises (like a mower that changes pitch and volume) without killing the speech. For now, dtln-rs is more of a developers’ library than a ready-made plugin, but its presence means developers of audio software have a free, high-quality option to integrate. If you’re tech-savvy, you could compile dtln-rs or similar and run it as a standalone filter for your audio streams.
  • Facebook’s Waveform Denoiser – In 2020, Facebook AI Research released an open-source project simply called denoiser (based on a paper “Real-Time Speech Enhancement in the Waveform Domain”). It used a variation of their Demucs neural network (originally for music source separation) to do speech noise removal. This was noteworthy because it operated directly on waveforms instead of spectrograms, and achieved good results. The code was available on GitHub and could run in real time on a GPU. However, it was a heavier model not really intended for lightweight deployment – and as of 2023 that repository has been archived (no longer actively maintained). Still, it demonstrated that even GANs and advanced models could be applied to noise suppression. Its quality was high, but the computational cost made it less practical for everyday use compared to the above options.
  • Others: The WebRTC project’s noise suppression module (by Google) is open-source and widely used in video conferencing apps. It’s not AI-based (more traditional DSP), but it’s optimized for speech calls. There’s also older SpeexDSP noise reduction (open-source DSP from Xiph). Both are decent for steady noise but not nearly as powerful as the AI solutions. On the AI front, companies like Intel have contributed; for example, Intel developers helped optimize DeepFilterNet for use with OpenVINO (taking advantage of CPU optimizations). We’re also seeing independent tools like NoiseTorch (which actually uses RNNoise under the hood) making it easy to create a virtual “clean” microphone on Linux. The ecosystem is vibrant – and because it’s open, a motivated user or developer can always tweak a model or combine techniques to fit their specific audio scenario.

Proprietary Heavyweights: Krisp and NVIDIA RTX Voice

While open-source solutions are rapidly improving, it’s worth comparing them to the popular proprietary noise suppressors that many musicians or streamers might already be using.

Krisp – You may have heard of Krisp as that app which mutes your Zoom call dog barks or keyboard clacks. Krisp is a commercial AI noise suppression technology that has been productized in an extremely user-friendly way. It runs on your device (no internet needed) and creates a virtual microphone that you can select in any app. In terms of quality, Krisp is considered one of the best for speech – it can handle everything from background chatter to door slams with minimal voice distortion. It uses deep learning models that have been highly optimized to run in real-time on CPUs. The company doesn’t divulge details of the model, but user reports and demos show it’s very effective while preserving the natural tone of the voice (no robot sound). Krisp’s SDK is available to developers (with approval) to integrate into apps, and it supports a wide range of platforms (Windows, Mac, Linux, iOS, Android, web browsers) – though notably not Safari or some low-power boards, according to their docs. For an end user, Krisp is mostly “set and forget”: turn it on, and it cancels noise system-wide. Latency is low (on the order of 20ms or so) which is fine for conversation or even live streaming. If you tried to monitor yourself singing through Krisp, you might notice a tiny delay, but it’s still quite small. CPU usage is moderate; it’s doing heavy computation, but modern CPUs handle it. One consideration: Krisp’s free tier allows a certain number of minutes per week of noise cancellation; beyond that it requires a subscription. Also, because it’s closed-source, you can’t fine-tune how it works – you get the default “balanced” noise removal it provides. In practice, Krisp rarely needs tweaking anyway, but audio purists might wish for a strength knob (which currently isn’t exposed). In DAW integration, Krisp isn’t a plugin; you’d use it by routing your input through the Krisp virtual device. That’s great for live calls or streams. For studio mixing, you probably wouldn’t use Krisp on a recorded track – you’d use an offline noise reduction plugin that gives more control. But if you’re in a pinch recording a podcast or demo and there’s background noise, running your mic through Krisp while recording could save you a lot of cleanup work later.

NVIDIA RTX Voice / Broadcast – If you have a gaming PC or high-end laptop with an NVIDIA RTX graphics card, you have access to NVIDIA’s noise removal tech. NVIDIA’s RTX Voice (now part of the NVIDIA Broadcast suite) uses a proprietary AI model accelerated by the GPU’s Tensor Cores to eliminate background noise in real-time. It’s essentially NVIDIA’s answer to Krisp (they even launched it free during 2020’s remote work boom). In terms of audio quality, RTX Voice is excellent. People have demonstrated extreme tests – like crinkling potato chip bags, using a blender, or blasting music – with only their voice coming through on the other end. It tends to introduce very few artifacts; voices stay quite natural unless the noise is literally as loud as the voice. The big advantage here is that the heavy lifting is done on the GPU, so your CPU is free for your DAW, soft synths, etc. The latency is also very low (a few milliseconds beyond the frame size) – gamers use it for live voice chat with no sync issues, so it’s safe to say it’s live-performance friendly. The catch, of course, is you need an NVIDIA GPU of the RTX series (or newer). That excludes Mac users, anyone with AMD graphics, or folks on older NVIDIA cards. Also, RTX Voice officially supports Windows (though NVIDIA has released plugins for OBS on Linux as well). Integration-wise, NVIDIA Broadcast creates virtual devices similar to Krisp. You enable the noise reduction in their app, then select the “NVIDIA Broadcast” mic in your programs. That means you can use it in any DAW or streaming software by choosing that input. One thing to note: the NVIDIA Audio Effects SDK that underlies RTX Voice is actually available to developers and is even open-source in terms of code. But it’s limited to NVIDIA hardware and requires that any app using it shows an “NVIDIA” logo (branding requirement). So, it’s not truly open in the sense of running anywhere, but if you’re coding an app specifically for RTX GPUs, you could integrate their noise removal. For the average user, though, it comes down to: do you have the right GPU? If yes, you get a high-quality noise eliminator essentially for free. If not, this option is off the table.

Other Proprietary Solutions – A quick honorable mention: there are professional plugins and tools like Cedar DNS or Waves Clarity Vx that also use AI for noise reduction in studio scenarios. These tend to be offline (not live) and focused on post-production (for example, cleaning up dialog or field recordings). They can be pricey, but they offer more control over the process (letting you dial in how much noise to remove, focus on certain frequency ranges, etc.). iZotope RX is another staple for post-production noise reduction (using advanced algorithms, though not strictly “AI” in the deep learning sense until recently). These tools are beyond our main scope here, but it’s good to know the landscape: big companies are leveraging AI too. Interestingly, their algorithms are often proprietary, whereas the open-source ones we discussed publish their methods openly. The gap in performance between open and closed is narrowing fast. In fact, some in the community point out that older open models like RNNoise, while effective, “are not nearly as effective as the modern solutions deployed in collaborative tools by larger companies” – but newer open models (DeepFilterNet, GT-CRN, etc.) are catching up to those “modern solutions.”

Practical Considerations (Quality, Latency, CPU/GPU Load, Integration)

From a musician or mix engineer’s perspective, how do these tools stack up in day-to-day use? Let’s break down a few key factors:

  • Noise Reduction Quality: All these tools aim to reduce noise without harming the main audio. The open-source algorithms have made great strides here. RNNoise will handle a mild constant noise very well, but under heavy noise it may leave some residual hiss or make the voice sound tinny. DeepFilterNet and GT-CRN improve on this, managing to remove more noise while keeping the speech intelligible and natural-sounding. In some cases, they can suppress noises that even some proprietary filters struggle with (like intermittent clatter or music in the background). Proprietary tools like Krisp and RTX Voice are tuned for maximum quality – in user tests, people often can’t tell any processing is happening except that the noise is gone. One thing to remember: if you try to use these voice-trained suppressors on something like a solo instrument recording, results vary. They’re primarily trained on speech. If you feed, say, a recording of a solo violin with air-conditioning noise, the AI might treat the violin like “voice” (so it stays) and the AC as noise (so it’s reduced). That would be good. But if you have a guitar playing and a violin in the “background,” it might not know which is the foreground “voice.” For music sources, these tools aren’t as dependable as for speech. Still, they can be handy for cleaning instrument tracks with steady background noise (tape hiss, amp buzz, etc.). You might lose a tiny bit of brilliance or sustain, but it can save an otherwise unusable take. The quality bottom line: properly used, these AI noise suppressors can dramatically improve clarity in live settings and save time in editing. The open tools are nearly on par with the proprietary ones for many scenarios, especially the latest models. For mission-critical pro work (like a film dialogue cleanup), engineers might still reach for the proven big-name tools, but for everyday streaming, recording, and mixing, open-source is often “good enough” and getting better.
  • Latency: All the tools we discussed operate in or near real-time. They typically process audio in small blocks of 10–20 milliseconds. There’s usually no noticeable delay introduced beyond that block size. For example, RNNoise in OBS doesn’t cause any sync issues with video. DeepFilterNet was designed for low-latency conferencing use, so it similarly can run with total processing delay well under 50 ms. GT-CRN’s streaming mode shows a 0.07 real-time factor on a CPU with no extra buffering, implying it’s effectively instant from the user’s perspective. Krisp and RTX Voice are also real-time; gamers and musicians use RTX Voice while performing without complaints about delay. If you are using any of these in a live performance monitoring situation (say, you want to hear your own vocals noise-reduced in your in-ear monitors), it’s best to test it first. A few milliseconds can be perceptible to very sensitive performers, but generally we’re talking 20ms or less for most of these, which is like standing 20 feet from your speaker – usually acceptable. The bottom line: latency is basically a non-issue with modern noise suppression plugins/filters; they’re all designed to act immediately.
  • CPU/GPU Resource Usage: This is where differences emerge more. RNNoise’s big selling point is it’s incredibly light – even a Raspberry Pi can run it. It might consume a few percent of one CPU core on a typical desktop, which is trivial. DeepFilterNet, being larger, could use more like 10-20% of a core (depending on your CPU and optimization). It’s still quite feasible on a modern machine. Some folks have run DeepFilterNet on small ARM processors with success, but if you throw it on a busy DAW session, keep an eye on CPU headroom. GT-CRN, amazingly, goes back to near RNNoise-level resource use. Its whole design is about ultra efficiency, so you can run it without worrying about CPU at all – ideal if you’re streaming from a laptop that’s also running soft synths and effects. DTLN-based solutions are somewhere in between; the Datadog dtln-rs can leverage optimizations like TensorFlow Lite and even cores on your machine to run faster. Their measurement of 33 ms to process 1 s of audioon Apple M1 is effectively ~3% CPU load, which is excellent. Krisp uses CPU as well, and while they don’t publish numbers, users have noted it can use perhaps 10-15% of a core when active (that will vary by machine). It’s the price for its sophisticated model, but usually not a big deal unless you’re already maxing out your CPU with other tasks. NVIDIA’s solution shifts load to the GPU. If you have a capable GPU, running RTX Voice might only add a small fraction to the GPU’s utilization (leaving your CPU untouched). However, if you’re also using that GPU for heavy graphics (or running a DAW with GPU-accelerated visuals, etc.), then it shares that resource. In most audio workflows, the GPU isn’t heavily taxed, so it makes sense to offload noise suppression there. An RTX 2060 or better will handle noise removal easily alongside, say, your video rendering or game.
  • Integration and Workflow: This might be the deciding factor for many. Open-source tools sometimes require more effort to integrate into your setup:
    • Plugins: RNNoise can be found as a VST plugin via community projects, and can be loaded in a DAW insert slot. But for others like DeepFilterNet, at the time of writing, you might not find a ready-made VST/AU. You may need to use a standalone app or command-line tool that processes audio files (for offline cleaning) or set up a virtual audio device on your OS for live use. For example, on Linux, there’s EasyEffects which offers a “Deep Noise Removal” filter (using DeepFilterNet under the hood) that you can apply to any input source at the system level. Projects like Jitsi have shown integrating RNNoise in software is possible and effective, which means we might see more user-friendly wrappers soon.
    • Proprietary apps: Krisp and NVIDIA Broadcast are very user-friendly but somewhat opaque. You turn them on/off outside your DAW. If you’re recording, you have to remember that the audio coming in is already being altered. One common workflow for musicians is to record both the raw and the processed tracks (using virtual audio devices) – that way you have a safety copy of the original in case the noise reduction accidentally cut something you wanted. But if you trust it, you can simply record the processed output and save time on editing.
    • Live Performance: In live sound situations (say a live-streamed concert or a DJ set with a mic), these tools can help manage noise from fans or crowd. Many streamers already use them to keep their mic feed clean. In a traditional stage concert, you probably wouldn’t run a vocalist through a noise suppressor due to the slight risk of artifacts; instead you’d use gating and good mic technique. But for online performances or rehearsals in noisy environments, they’re a boon. Integration there might just mean running your mic through OBS or a similar host with the filter engaged.
    • DAW Mixing: If you’re mixing a multitrack recording after the fact, you might opt for offline noise reduction (like using RX or even sending the track through an AI model offline) rather than a real-time suppressor. However, if a track has constant background noise, you could insert something like RNNoise in the channel and let it run dynamically. Just use your ears – if it starts to degrade the audio, you might need a more surgical approach. The nice thing is, open-source tools give you flexibility. You can experiment without licensing issues. Want to run DeepFilterNet on each stem of a noisy live recording? You could script that and do it, no extra cost.
  • Adjustability: Most AI noise suppressors (open or closed) don’t give the user a lot of knobs. Typically it’s just an on/off or maybe a strength slider. RNNoise in OBS has no settings aside from mode (it has an “RNNoise” mode vs “speex” vs “basic” in OBS). Krisp and RTX Voice basically have an on/off and maybe a dial for how aggressive to be. The rationale is the AI figures out the optimal filtering internally. Some open projects like DeepFilterNet and DTLN libraries allow developers to tweak parameters or thresholds (as seen in EasyEffects’ UI for DeepFilterNet, there are advanced settings for attenuation limits, etc.). But for a casual user, it’s mostly automatic. This is great for ease of use, but if you enjoy tweaking, you might feel a bit hands-off. One tip is to simply manage how you feed the audio: e.g., if a noise suppressor isn’t catching a certain periodic noise, you might preprocess with a traditional EQ or filter to help it out (remove the worst offending frequency so the AI has an easier job). Conversely, if the suppressor is chewing on your reverb tails or breathing noise that you actually want in the recording, you might choose to only enable it for certain sections or automate its mix.

In summary, the open-source tools are quickly approaching parity with proprietary ones on the key practical metrics. RNNoise paved the way with ultra-low latency and CPU impact, at the cost of some quality. Newer models like DeepFilterNet and GT-CRN improve quality significantly while still keeping latency low and performance reasonable – GT-CRN in particular is a standout for being both tiny and powerful. Proprietary options like Krisp and RTX Voice remain more polished and accessible (no compiling code or routing hacks needed), which can be critical if you just want something that works with minimal fuss. Depending on your setup and willingness to experiment, you might choose one of the open solutions to integrate into your workflow, or stick with a commercial app for convenience.

Conclusion and Recommendations

In the battle of open-source vs proprietary AI noise suppression, there isn’t a one-size-fits-all winner – but the good news is we have plenty of great options. For musicians and audio professionals, these tools can be genuine problem-solvers:

  • If you’re live streaming or recording in a noisy home studio: An easy win is to try something like RNNoise (free) or Krisp (has a free tier). RNNoise, via OBS or a plugin, can handle typical PC background noise with virtually no CPU cost. If you notice artifacts in your voice, you can dial it back or consider Krisp for a more advanced treatment. Krisp and RTX Voice will give you a very clean result with little tinkering – ideal if you’re not an audio tech guru and just want it solved.
  • If you need the best open-source quality: Experiment with DeepFilterNet or the latest GT-CRN. They might require a bit more setup, but they can outperform older methods, especially for non-stationary or loud noises. As these projects evolve, expect them to become more user-friendly. You might soon see plugins or built-in features in digital audio software that utilize these algorithms (just as OBS integrated RNNoise). By getting familiar with them now, you’re ahead of the curve.
  • Resource considerations: On a low-power device (like a cheap laptop or single-board computer), RNNoise or GT-CRN are your friends due to their tiny footprint. On a powerful rig or one with an RTX GPU, you have the luxury of using a more demanding model – you could even run multiple noise suppression instances on different tracks if needed. Always monitor your CPU/GPU usage when adding these to a live project, just to ensure you’re not introducing risk of dropouts.
  • In the studio vs on stage: For polished studio work, you might still prefer to use dedicated post-processing noise reduction (and invest time in fine-tuning it) for the absolute best result. However, the gap is closing. The AI models we discussed are essentially doing what those offline tools do, but instantly. It’s not far-fetched to imagine using, say, DeepFilterNet inside your DAW while tracking a vocal, to give the singer a noise-free monitor mix, and possibly even commit that to recording to save time. If you do this, keep a copy of the raw audio just in case. In live sound reinforcement (on stage), these tools aren’t common yet – but who knows, maybe digital mixers will incorporate AI noise suppression for open mics in the future. For now, they’re mostly a studio/live-stream aid.
  • Usability: If you value ease-of-use and support, a proprietary solution might be worth it. Krisp’s one-click approach is very appealing if you don’t want to fiddle. NVIDIA’s solution similarly “just works” if you have the hardware. Open-source tools might require checking GitHub or forums if you encounter issues, which some are fine with and others not so much. That said, open-source means the community is constantly improving and documenting things – you might even contribute or suggest features!

In real-world audio production scenarios, AI noise suppression tools offer a kind of safety net. They won’t replace good microphones or proper acoustic treatment, but they can make a night-and-day difference in challenging conditions. Imagine being able to use that take where a truck drove by outside, because the AI cleaned it up, or being able to hold a jam session on a busy street and still get usable audio. We’ve reached a point where that’s possible with a laptop and free software. The tone among many users is one of pleasant surprise – “I can’t believe the noise is just… gone.” As these tools continue to evolve, we can expect even better performance (more types of noise handled, even fewer artifacts) and more integration into the audio software we use daily.

Final tip: always trust your ears. Use noise suppression as a tool to serve the mix or performance, and not as an automatic cure-all. Sometimes a bit of ambient noise is part of the character of a recording; you might not want to remove all of it. But when you do need silence behind the music, these open-source AI solutions and their commercial counterparts are like having a smart audio engineer assistant at your side, constantly riding the fader on the noise. And that is pretty awesome for those of us who just want our music and dialog to shine. Happy mixing, and may your recordings be noise-free (or at least, noise-managed)! 

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