Welcome back to My Weird Prompts, the podcast where our producer Daniel Rosehill sends in the questions that keep us up at night. I'm Corn, and I'm joined as always by Herman Poppleberry. Today we're tackling something that's become increasingly relevant, especially if you're trying to get work done in less-than-ideal circumstances - we're talking about background noise removal in voice recording and transcription technology.
Yeah, and what makes this particularly timely is that we're living in this moment where voice-to-text has become genuinely useful for productivity, but the real-world conditions people use it in are... messy. Literally, in this case - there's a baby involved in the prompt.
Right! So the core question here is really about strategy. When you're trying to clean up audio for transcription purposes, do you remove the noise in real-time as it's coming in, do you do it after the fact during post-processing, or do you rely on hardware microphones that claim to have built-in noise suppression? Which approach actually makes the most sense?
And I think the answer is more nuanced than people realize. It's not like one approach is objectively better across all scenarios. The trade-offs are real, and they're worth understanding.
Okay, so let's start with the basics. When we talk about background noise removal, we're essentially talking about neural networks doing computational magic, right? That's the technology powering most of these solutions these days?
Well, yes and no. I mean, neural networks are definitely the cutting edge of what's possible right now, but they're not the only approach. You've got traditional signal processing methods - things like spectral subtraction, Wiener filtering - those have been around for decades. But you're right that neural networks have really revolutionized this space because they can adapt to different types of noise in ways that older methods just couldn't.
So when I'm using something like the AI Noise Remover app on Android, or ElevenLabs' Voice Isolator - those are neural network based?
Exactly. Those are using deep learning models trained on massive amounts of audio data to identify what's speech and what's noise, and then they suppress the noise while preserving the speech. The sophistication there is actually pretty remarkable. But here's where I'd push back on something - a lot of people assume that because these tools use neural networks, they're automatically superior to everything else. The reality is more complicated.
How do you mean?
Well, neural networks are incredibly powerful, but they're also computationally expensive. They require processing power. So when you're trying to do noise removal in real-time on a mobile device like an Android phone, you're hitting some hard constraints. You can't just run a massive deep learning model while simultaneously recording audio and keeping the rest of your phone responsive.
Right, so that's where the trade-off comes in. Real-time processing has to be faster, which means maybe less sophisticated?
Exactly. Real-time noise removal, especially on device, has to be optimized for speed. You're looking at smaller, more efficient models. They work pretty well, but they're not going to be as effective as what you could do if you had unlimited processing time to work with the audio after the fact.
But here's what I'm curious about - if I record something on my phone in a noisy environment, and then I upload it to something like ElevenLabs or another cloud-based service, they can apply heavier neural network models to clean it up?
Yes, and that's actually a really important distinction. Post-processing, especially when done in the cloud, can use much more sophisticated models because you're not constrained by the need for real-time responsiveness. You can throw computational resources at the problem. The trade-off is latency - there's a delay while the audio gets processed.
So for someone like Daniel, who's trying to dictate an email to his accountant while holding a fussy baby, real-time noise removal might be the way to go because he needs the transcription quickly. But the quality might not be perfect.
Right, but - and this is important - real-time deep learning noise suppression has actually come a long way. There was some really interesting research showing that you can do real-time noise suppression without even needing a GPU. Modern mobile processors are powerful enough to run reasonably sophisticated models in real-time. So it's not like you're choosing between "fast but terrible" and "slow but perfect."
What about the hardware microphone angle? Like, some microphones claim to have built-in noise cancellation. Are those actually useful, or is that more marketing speak?
That's a great question, and honestly, hardware-based noise cancellation has its place, but I think software has largely surpassed it for most use cases. Here's why - a microphone with hardware noise cancellation is typically using some kind of analog circuit or a very simple digital filter. It's fast, it doesn't require much power, but it's not intelligent. It can't really distinguish between speech and noise the way a neural network can.
So it's just kind of... blanket noise reduction?
Basically, yeah. It might suppress everything below a certain frequency, or it might try to detect sudden loud noises, but it doesn't have the contextual understanding that AI-based systems have. A neural network trained on thousands of hours of audio can learn that a baby crying has certain acoustic characteristics, and it can be more surgical about removing it while preserving the rest of the audio.
Although, wait - wouldn't hardware-based filtering be faster? Like, there's no latency with a hardware microphone doing its thing?
There is some latency, actually. And more importantly, there's no recovery from mistakes. If the hardware microphone decides something is noise and removes it, that's it. With software-based approaches, especially post-processing, you can be more conservative, you can tweak parameters, you can even manually review what happened.
Hmm, but I'm thinking about the practical scenario here. If I'm using voice-to-text on my phone right now, I'm probably not uploading to the cloud and waiting for post-processing. I'm probably using whatever real-time solution is built into my phone or my voice recording app.
Right, and that's where I think the practical answer becomes clear. For most people, most of the time, real-time on-device noise removal is the way to go. It's fast enough, it's good enough, and you get your transcription immediately. The post-processing approach is better if you have the time and if quality is critical.
So in Daniel's case, he needs the transcription quickly to send to his accountant. Real-time noise removal on the Android app he's using would probably be his best bet?
Absolutely. He records the message, the app cleans it up in real-time or near-real-time, he gets a transcription, he sends it off. Done. The baby noise might still degrade the transcription accuracy a bit, but it'll be way better than if he didn't use any noise removal at all.
But let's talk about the actual accuracy impact. I'm curious - if you've got a neural network trained to remove background noise, how much does that actually improve transcription accuracy for something like speech-to-text?
So, this is where things get really interesting. The relationship between noise removal and transcription accuracy isn't always linear. Like, you'd think that if you remove 50% of the noise, your transcription accuracy improves by some proportional amount. But it doesn't work that way in practice.
Why not?
Because transcription models - the speech-to-text engines themselves - are also trained on noisy audio. They've learned to handle some amount of background noise. So if you over-process the audio and remove too much, you can actually introduce artifacts that confuse the transcription model. You end up with audio that sounds cleaner to human ears but actually performs worse with ASR.
Wait, so you can have audio that sounds worse to a person but transcribes better?
Exactly. This is counterintuitive, but it happens. The sweet spot is usually somewhere in the middle - enough noise removal to help the transcription model, but not so much that you're introducing processing artifacts.
That's wild. So the optimal noise removal isn't necessarily the one that sounds best?
Not necessarily. And this is why the research in this area is still really active. People are trying to figure out the optimal balance. Some of the newer approaches are actually training noise removal models and transcription models together, so they're optimized for each other rather than being separate steps.
Okay, so let me try to synthesize this. We've got three approaches - real-time on-device, real-time in the cloud, and post-processing in the cloud. Real-time on-device is fastest but maybe less sophisticated. Cloud-based real-time might be better quality. Post-processing could be the best quality but with latency. Is that fair?
That's a reasonable summary, but I want to add some nuance. Real-time in the cloud isn't really a thing in most cases. You're either doing real-time on-device or post-processing. The distinction is usually on-device versus cloud. And the other thing is that "cloud-based" doesn't necessarily mean better. It means you have more compute available, but the actual model being used matters more than where it's running.
Right, good point. So if I'm using Voice Isolator by ElevenLabs, for example, am I doing real-time processing or post-processing?
It depends on the tool. Some of their products are real-time, some are batch processing. But the general trend in the industry is moving toward real-time on-device processing because... well, privacy, latency, cost. You don't want to be uploading all your audio to the cloud if you don't have to.
That's a good point about privacy. If I'm using my phone to record a message about sensitive financial stuff, I probably don't want that going to a cloud service.
Exactly. And that's another reason why on-device processing has become more popular. The models are getting smaller and more efficient, so you can run them locally without needing tons of processing power.
Alright, let's take a quick break from our sponsors.
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...Alright, thanks Larry. So where were we?
We were talking about the practical trade-offs between different noise removal approaches. I think one thing I want to circle back to is the Android-specific challenge that was mentioned in the prompt. Android is apparently trickier for real-time audio processing than iOS?
Yeah, that's actually true, and it's worth understanding why. iOS has a more controlled environment - Apple controls both the hardware and the software, so they can optimize audio processing pipelines. Android is fragmented. You've got hundreds of different devices with different processors, different audio hardware, different versions of the OS. It's harder to build something that works consistently across all of them.
So does that mean Android users are stuck with worse options?
Not necessarily stuck, but they have fewer first-party options. Apple's built-in voice memo app has pretty solid noise cancellation. Android doesn't have an equivalent built into the OS. But there are good third-party apps. The AI Noise Remover app I mentioned earlier is actually quite good. So is Voice Isolator. They work around the fragmentation by being optimized for the most common device configurations.
But the latency issue is still there, right? Like, if you're trying to do real-time processing on Android, you're fighting against the OS architecture?
You're fighting against it somewhat, but it's getting better. Modern Android processors are legitimately powerful. The Snapdragon chips in current flagships can handle neural network inference pretty easily. The issue is more about consistency - you might have great performance on a flagship phone and mediocre performance on a mid-range device.
So if Daniel's using an older Android phone, he might get worse results?
Possibly, yeah. Or he might want to consider using a cloud-based service where the processing happens on powerful servers regardless of his phone's specs.
But then he's back to the privacy issue and the latency issue.
Right, so it's trade-offs all the way down. There's no perfect solution that works for every scenario.
I think that's actually an important point to make. Let me ask you this - if you were advising someone in Daniel's situation, what would you tell them to do?
Honestly? I'd tell them to try a few approaches and see what works best for their specific use case. But if I had to pick one, I'd probably recommend using a real-time on-device solution if they have a reasonably modern Android phone. Something like AI Noise Remover or Voice Isolator. These apps are good enough that you'll get a transcription that's clean enough to send to an accountant, and you get it immediately without uploading anything to the cloud.
And if they don't have a modern phone?
Then maybe explore cloud-based options, or accept that some transcription errors will happen and just do a quick manual review of the transcription before sending it.
Yeah, that's practical. I mean, if you're dictating an email, even if there are a few errors, you can probably catch them and fix them quickly.
Exactly. The goal isn't perfect transcription - it's good enough transcription that saves you time compared to typing.
Alright, so we've covered the technical landscape. Let me ask a bigger picture question - do you think the state of noise removal technology is actually good enough now that people should be regularly using voice-to-text in less-than-ideal environments?
I think it's good enough for a lot of use cases. If you're in a moderately noisy environment - a coffee shop, an office with background chatter - yeah, modern noise removal is quite good. If you're in a really extreme environment - like, a construction site or a loud concert - then you're probably going to have trouble no matter what.
And what about the baby crying scenario specifically? That's a pretty intense acoustic environment.
Baby crying is actually... it's a specific challenge because it's high-pitched, it's unpredictable, and it can be very loud. The neural networks have probably seen training data with babies crying - this is a common real-world scenario - so they should handle it reasonably well. But it's going to degrade accuracy compared to a quiet environment.
So Daniel's not going to get perfect transcription, but it should be better than nothing?
Right. And honestly, I think that's the realistic expectation for voice-to-text in less-than-ideal environments. You're not going to get transcription quality that's as good as if you had recorded in a quiet room, but you can get something that's useful and saves time.
Let me bring up something that occurred to me while you were talking. You mentioned that transcription models are trained on noisy audio, so they can handle some noise. Is there a scenario where you'd actually want less noise removal rather than more?
Yeah, I think so. If you're using a transcription model that's trained on a certain level of background noise, and then you apply very aggressive noise removal, you might be moving the audio away from what the model expects. It's like... you're making the audio cleaner, but also weirder from the transcription model's perspective.
That's a really interesting point. So there's an optimal level of noise removal that's not the maximum possible noise removal?
Exactly. And that's something I wish more people understood about this technology. The intuitive thing to do is "remove as much noise as possible," but that's not always the right approach.
Has there been research on finding that optimal level?
There's been some work on it, yeah. Some researchers have been training noise removal and transcription models jointly, so they learn what level of noise removal is actually optimal for downstream transcription. But it's not something that's widely implemented in consumer tools yet.
So most consumer apps are just trying to remove as much noise as possible?
I think they're trying to balance removing noise while not introducing too many artifacts. But yeah, they're not necessarily optimizing specifically for transcription accuracy. That's a more specialized approach.
Interesting. Alright, we've got Jim on the line. Jim, what's on your mind?
Jim: Yeah, this is Jim from Ohio. Look, I've been listening to you two go on about all these fancy neural networks and cloud processing and all that, and I gotta say, you're way overthinking this. Back in my day, we just spoke clearly and made sure we weren't in a noisy environment. That's it. Also, we got a cold front moving through Ohio today, really knocked my heating bill up, but that's beside the point. But seriously, why can't people just wait until they're in a quiet place to record their messages?
Well, that's a fair point in theory, Jim, but in practice, sometimes you can't wait. If Daniel's holding a baby and he needs to send something urgent to his accountant, he might not have the luxury of waiting until later when it's quiet.
Jim: Yeah, but... he could just type it. I mean, we've got keyboards. We've got our fingers. What happened to just typing?
I get what you're saying, but the whole point of voice-to-text is that it's faster when your hands are full. If you're literally holding a baby, you can't type.
Jim: Ehh, I don't know. Seems like a solution in search of a problem to me. Also, my neighbor Gary has the same baby situation, and you know what he does? He just puts the baby down and types. Problem solved.
But that's not practical for everyone, Jim. Some people don't have a safe place to set the baby down. Some people need both hands.
Jim: I'm just saying, maybe the real solution is to not try to do two things at once. My cat Whiskers has the right idea - when she's doing something, she focuses on that one thing. She doesn't try to write emails while eating. Well, she does try, but it's messy.
That's... actually not a bad life lesson, Jim. But I think for people who do need to multitask, this technology is genuinely useful.
Jim: Fine, fine. I'm not saying it's not useful. I'm just saying maybe people are too reliant on technology to solve problems that wouldn't exist if they just planned better. But what do I know? I'm just a guy from Ohio with a cat and bad knees.
Thanks for calling in, Jim. We appreciate the perspective.
Jim: Yeah, alright. Good luck with all that neural network stuff.
So, Jim's not wrong that planning ahead would solve some of these problems. But I think there's a real value in having tools that let you be productive in non-ideal circumstances.
Yeah, I mean, real life is messy. Babies cry. You're in coffee shops. You're in cars. The technology that works in those real-world scenarios is valuable, even if the ideal scenario is to be in a quiet room.
Exactly. And I think that's actually been a big driver of voice-to-text adoption - it lets people capture thoughts and communicate when they otherwise couldn't.
So, let's talk about practical takeaways. If someone's listening to this and they want to improve their voice-to-text experience in noisy environments, what should they actually do?
First, try the built-in noise cancellation on your device. Most modern phones have something. If that's not enough, try a dedicated app like AI Noise Remover or Voice Isolator. See what works for you.
And if you're using Android specifically, which the prompt mentioned?
Same advice. There are good third-party apps available. You might have to pay a few dollars, but it's worth it if you're regularly recording in noisy environments.
What about the post-processing angle? Is there a scenario where someone should be uploading their audio to a cloud service for processing?
If you have time and quality is critical, yeah. If you're recording something that needs to be perfect - like, a podcast episode or a professional voice memo - then post-processing with a more sophisticated tool makes sense. But for quick transcriptions that are good enough to send to your accountant, on-device real-time processing is probably sufficient.
And what about hardware microphones with built-in noise cancellation? Should people bother with those?
I'd say they're worth considering if you're regularly recording in very specific environments - like, if you're always recording in a car, a car-specific microphone might be worth it. But for general use, software solutions are more flexible and usually better.
So the takeaway is: try the software solutions first, see what works for your specific situation, and don't stress if it's not perfect?
Yeah, that's a good summary. And remember, the goal is usually just good enough, not perfect.
I think that's actually a really important mindset shift. A lot of people might think voice-to-text has to be perfect to be useful, but in reality, it just has to be better than the alternative.
Right. If you're choosing between dictating something and letting work pile up because you're too busy holding a baby, dictating with imperfect transcription is the clear winner.
Alright, so looking ahead, where do you think this technology is going? We've got real-time neural network processing on phones, we've got cloud-based solutions, what's next?
I think the next frontier is better integration between noise removal and transcription. Like, models that are specifically trained to work together. We'll probably also see better on-device processing as phone processors get more powerful. And there's interesting research into multi-modal approaches - using video or other sensors to help identify and remove noise.
Multi-modal? Like, the phone would know you're holding a baby and adjust its noise removal accordingly?
Exactly. Or the camera could detect that you're in a coffee shop and apply different noise removal parameters. That kind of contextual intelligence.
That's pretty cool. But that's probably a ways off?
It's being researched now, so maybe a few years before it becomes mainstream. But I think that's the direction things are heading.
Well, this has been really interesting. I came in thinking this was going to be a straightforward question about which approach is best, but it turns out it's much more nuanced than that.
Yeah, it's one of those areas where the technology is genuinely useful but also not a magic bullet. Context matters. Your specific situation matters. The device you're using matters.
Which is probably why Daniel sent in this prompt - because he was grappling with these trade-offs in real time.
Absolutely. And I think the fact that he's already thinking about this stuff, already using voice-to-text productively, means he's ahead of most people in terms of leveraging this technology.
Yeah, the people who are really going to benefit from better noise removal are the ones who are already trying to use voice-to-text in challenging situations.
Right. And as the technology improves, I think we'll see more and more people in that category. Voice-to-text is becoming genuinely practical for real-world use.
Alright, I think that's a good place to wrap up. Thanks to our producer for this fascinating prompt - it's given us a lot to think about. And thanks to Herman for diving deep into the technical details.
Happy to be here. And thanks to Jim for keeping us grounded with his skepticism.
That's My Weird Prompts. You can find us on Spotify and wherever you get your podcasts. We'll be back next week with another prompt. Until then, try not to overthink your noise removal settings.
Or do overthink them. Whatever works for you.
Fair enough. See you next time.
