Ian Sampson Posted March 21 Report Share Posted March 21 Hi everyone! Just wanted to share an app I made that post-production folks might find useful. It’s called Hush, and it uses AI to automatically remove background noise and reverb from dialogue (and other spoken audio) — with minimal artifacts. I designed the model myself, and trained it on a large dataset of common noise types: ventilation, traffic, honking horns, barking dogs, chirping birds, etc. — as well as room reflections from a wide variety of indoor spaces. You can hear a quick demo over on the website. The model continues to evolve as I add more samples to the training data. I’m always open to suggestions, and happy to fine-tune it for specific use cases wherever possible. For example, I have another module in the pipeline to handle lav-related noise: clothing rustle, muffled dialogue, etc. The app itself is a batch processor with a simple drag-and-drop interface. It can handle single files or many at a time. On Apple Silicon Macs (highly recommended, if not strictly required), it runs entirely on the Neural Engine, which massively accelerates processing while leaving the CPU cool (and the fans off). It can also run on an external GPU. It’s been in beta for two months, and I’ve gotten some great feedback from dialogue editors, voice actors, and other folks who’ve used it in their projects. I just released the first public version on the Mac App Store today, for an introductory price of $49.99 US. You can also download a 21-day free trial, without any other restrictions, at hushaudioapp.com. As a solo developer, I put a lot of care into my work and really thrive on feedback — so if you have any questions or suggestions, please let me know :). Thanks, Ian Quote Link to comment Share on other sites More sharing options...
Philip Perkins Posted March 22 Report Share Posted March 22 I look forward to test driving this app, but I probably would never "batch" process any audio for NR in a movie mix. I'd want something like this to work in real time on individual tracks or clips of audio so I can hear what it is doing (or not doing) and be able to tweak what it does per clip or even per word if needed, with those tweaks being part of the mix automation. Will you make this into a real time VST plug in that can work within a DAW? Quote Link to comment Share on other sites More sharing options...
ElanorR Posted March 22 Report Share Posted March 22 Thank you for your hard work. It might be helpful to me in podcast editing. Some if these are not recorded in the best environment. Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted March 22 Author Report Share Posted March 22 Great question — it’s really helpful for me to hear how this might fit (or not fit) into existing workflows. And yes, I’d like to make it into a real-time plugin in the near future. I already have it working as a prototype AU plugin in Logic, but with really high latency (~500 ms) and you can only run one instance at a time without freezing tracks (at least on a base model M1 Mac Mini). A possible solution would be to make a lightweight version of the AI model, which could run more efficiently in real-time, and then switch to the full, high-quality version when you bounce. Trying to prioritize whether to work on the plugin next, or add a spectrogram-based editing interface for offline work with individual clips. Eventually I’d like to do both, but I have to start somewhere, and the plugin may well turn out to be the more useful path. Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted March 22 Author Report Share Posted March 22 21 minutes ago, ElanorR said: Thank you for your hard work. It might be helpful to me in podcast editing. Some if these are not recorded in the best environment. You’re very welcome! A few podcast editors tried out the beta over the last couple months, and they said it worked really well. It’s trained to handle common types of indoor noise (HVAC, fans, etc.) as well as room reflections, which are probably the main culprits in home podcasting setups. If the noise and reverb are moderate (e.g. using a mic in cardioid at a reasonable distance from the source), the reduction is typically really subtle, without any audible artifacts. That said, it’d probably struggle with audio recorded on, say, a cellphone, or with a laptop mic far away (which can happen, I suppose, with some remote podcast interviews). I don’t do podcasts myself, but I record voiceover and audiobook narration at home, and the app has been super useful in getting rid of all the room tone. Quote Link to comment Share on other sites More sharing options...
berniebeaudry Posted March 22 Report Share Posted March 22 Just listened to the demo on the website. Very impressive! I didn't hear any artifacts and the nuances of the recording were all intact. I work with most of the noise reduction programs that are out there and I look forward to potentially adding this one to the arsenal. Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted March 22 Author Report Share Posted March 22 1 hour ago, berniebeaudry said: Just listened to the demo on the website. Very impressive! I didn't hear any artifacts and the nuances of the recording were all intact. I work with most of the noise reduction programs that are out there and I look forward to potentially adding this one to the arsenal. Thanks for the kind words! That was my goal: to make the model subtle enough that you don’t lose any of the details of the original speech. The processing is designed to kick in only where needed: clean audio passes through unchanged, and moderately noisy audio gets processed pretty gently. (Very loud noise will still produce some artifacts, but hopefully less so than with traditional noise reduction algorithms.) Curious to hear what you think, if you get a chance to try it out :). Quote Link to comment Share on other sites More sharing options...
osa Posted March 22 Report Share Posted March 22 Ah brilliant! I am an old timer on mojave 10.14.6 any chance this might work on my system? I can run a trial when i get home. I love all things noise reduction would love to give this a trial. Pipe dream would be aax for pro tools even if audiosuite only but standalone app is just fine for me as well. Quote Link to comment Share on other sites More sharing options...
Philip Perkins Posted March 22 Report Share Posted March 22 If you decide to make a plugin (which is how I think most post-pros would use it), please consider making a VST version so non PT and Windows users can use it! Quote Link to comment Share on other sites More sharing options...
Mark LeBlanc Posted March 22 Report Share Posted March 22 If you could come down to the deep south and train this to insects!... This demo does sound impressive Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted March 23 Author Report Share Posted March 23 9 hours ago, osa said: Ah brilliant! I am an old timer on mojave 10.14.6 any chance this might work on my system? I can run a trial when i get home. I love all things noise reduction would love to give this a trial. Pipe dream would be aax for pro tools even if audiosuite only but standalone app is just fine for me as well. Sadly, the minimum macOS version is 12.0 (Monterey). If you’re curious to hear what it sounds like on some of your audio, though — and if you have something you don’t mind sharing — I’d be happy to process it and send it back. Yes, AAX would be cool for sure, and going the AudioSuite route would make it easier to get things working without the real-time constraint. Not on the immediate horizon, but I’ll look into it! Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted March 23 Author Report Share Posted March 23 7 hours ago, Philip Perkins said: If you decide to make a plugin (which is how I think most post-pros would use it), please consider making a VST version so non PT and Windows users can use it! VST and Windows support would be great for sure! At the moment, Hush is pretty heavily optimized for Mac (both at the software level, with CoreML, and at the hardware level, with the Neural Engine). Getting the AI to perform with any reasonable efficiency was only really possible by targeting a specific architecture, and taking advantage of the massive acceleration for machine learning on M1 and M2 Macs. I imagine you could get similar performance on PC using a discrete GPU, but that’d also mean rewriting the app from the ground up. Not impossible, of course! But not likely to happen soon, without help from other developers who know Windows better than I do:p. Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted March 23 Author Report Share Posted March 23 21 hours ago, Mark LeBlanc said: If you could come down to the deep south and train this to insects!... This demo does sound impressive Haha I just may! I actually have a massive library of insect recordings from another sound design project — if getting rid of cicadas & crickets is desirable I could probably train a model to do that :p. Quote Link to comment Share on other sites More sharing options...
Mark LeBlanc Posted March 25 Report Share Posted March 25 would love to hear the outcome of that training Quote Link to comment Share on other sites More sharing options...
Wandering Ear Posted April 2 Report Share Posted April 2 This is great. I can’t wait to audition it once I’m back in my studio this week. Out of pure curiousity, what do you use as training data to remove room reflections and reverb? Impulse responses? Dry and wet versions of the same recordings? Quote Link to comment Share on other sites More sharing options...
Ian Sampson Posted Tuesday at 08:22 PM Author Report Share Posted Tuesday at 08:22 PM Sorry for not replying before! This one slipped through the cracks. But yes, exactly — I take dry recordings and convolve them with impulse responses, then train the model to predict the dry signal from the wet one. Also just released an AudioSuite version of Hush — covered in a new thread. Quote Link to comment Share on other sites More sharing options...
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