How AI Noise Reduction Works
Table of Contents
Advancing technologies with learning capabilities is making substantial impressions in every industry. The digital landscape is expanding to include sensory features that explore visual and auditory innovation. That exploration includes AI (artificial intelligence), VR (virtual reality), and AR (augmented reality) technology.
These innovative computing technologies deliver ever-expanding possibilities. The transformation of the tech landscape is becoming harder to ignore. While audiences are paying close attention to the potential growth of AI tools and applications, one category is getting equal attention – noise-canceling AI.
In recent years, the unpredictable changes to the work environment have brought more attention to digital communications. Noise-canceling tech has been out for a while. Incorporating AI into the audio field enhances the current capabilities. Thanks to AI noise reduction, digital comms across the board have leveled up.
Using AI to reduce background noise may seem simple enough, but the work needed to achieve it is complex. Being able to communicate from a digital device typically requires a strong signal. But even a strong WiFi signal can't ensure clear audio transmission.
For a more in-depth understanding of how AI noise reduction works, we must dive into the history and fundamentals of this technology.
The history of AI noise reduction
Noise reduction technology has been around for decades, with noise-canceling headsets used in remote offices, hybrid workspaces, and on-site businesses for years. Throwing AI into the mix has enhanced the noise-reducing possibilities.
Of course, you can’t talk about the history of noise-canceling AI without understanding why it’s important. Clear audio on every platform is crucial to communication. Limiting the background noise for digital communications is the driving factor for this technology. No one wants to struggle to hear their colleagues while on a video call. Suppressing the background noise while hyper-focusing on human voices marked the next step in noise-reductive tech. Deploying artificial intelligence and machine learning techniques in the field helped achieve this.
The evolution of noise reduction techniques that led to AI integration began with a subset of AI called machine learning. AI and machine learning are often used interchangeably. It wasn’t until advanced machine-learning techniques became effective that we saw enhancements to decades-old noise-canceling technology.
AI noise reduction fundamentals
Put simply, noise reduction works to remove sounds from a signal. It helps eliminate background noise while remaining focused on desired sounds. A phone call will omit background noises without distorting the main audio in real time. For instance, the sound of barking dogs or traffic was the cause of interruptions. With noise-canceling AI, those sounds won’t be an issue.
There is a clear distinction between traditional noise reduction and AI-driven methods. The latter uses artificial intelligence algorithms to reduce background noise. These noise reduction methods are applied to audio recordings and images. Conventional noise-reducing techniques use subtractive algorithms that help isolate and enhance the dominant sound while lowering or removing background noise. Although not 100% precise, the results are what most people get from current noise-canceling devices.
Today, people might be most familiar with adaptive and active noise-canceling/reductive tools. Active noise canceling (ANC) detects background/external sounds and cancels them out before users can hear anything distracting. Adaptive ANC is more sophisticated. It allows the microphone and listening device to automatically adjust to the surroundings. Earbuds with this feature allow users to take calls while working outdoors, in a cafe, or while traveling.
AI-centric processes do the same using deep learning and fast-evolving algorithms. It more accurately determines the dominant sound, isolates it, enhances it, and eliminates ambient noises all at once. For instance, look at active noise-canceling AI. It works to detect the undesired sound. It then produces a sound that is on a different sound wave. When the out-of-phase noise meets the undesired noise, the sound waves neutralize, thus canceling each other out.
Various algorithms
AI runs on various machine learning subsets. The first level is machine learning (ML), and the next is deep learning (DL). Both involve sound analysis that the AI can learn from.
Machine learning algorithms, for instance, help the AI software understand and learn from past data. From there, it will adjust its responses. Examples of this algorithm appear on music platforms that suggest artists based on a user's preferences, searches, and listening history.
Deep learning algorithms are another level of machine learning, except they analyze even larger datasets. Those data pools are not only larger but are also more complex. DL is gaining ground as the more widely used algorithm for AI sound reduction technology. It’s earned the name the universal learning method, because it’s been such a useful technique in nearly every professional field.
DL methods are considered the scalable approach. That means because it’s able to learn and adapt to more complex information, it’s capable of increased data collection and computation. No matter which machine learning techniques are favored, each can help deliver a more precise audio and sound experience. As a multifaceted algorithm, it's even capable of finding solutions with limited or insufficient data.
Benefits of AI noise reduction
The main benefit of AI noise reduction is the overall improved sound quality. No more worrying about the clarity of past or live recordings from your PC audio device. Audio/video software that uses ML and DL models will significantly improve every communication session.
Eliminating the tedious nature of manual audio editing is another benefit of AI noise-canceling software. Scrubbing background noise is a manual step of the past. Today’s technology automatically accounts for it, so all you have to focus on is your work and the dominant sounds.
AI noise reduction technology will reduce or eliminate ambient noise in real time. Not only does it reduce or virtually eliminate that background noise, but it’s also beneficial for images. It’s a technology that can reduce noticeable noise in a picture. It’s an effective tool for removing grainy, blurry, or distorted background noise from images, graphics, photos, and similar mediums.
Fields of application
The most practical applications for noise canceling AI are phone calls or audio/video calls via tablet, PC, and desktop computers. Other common uses for advanced AI noise reduction technology include covering video conferences such as 3-way conference calls/meetings, pre-recorded or live podcasts, and even music production.
Finding technology that uses AI noise reduction isn’t difficult. Take the HP’s Dragonfly G4 laptop, for instance. The ultra-clear speakers and audio system are equipped with AI noise-reduction software.
Noise reduction AI software can also reduce the background noise of images, making it a useful tool for photographers. Similar fields, such as videography and filmmaking, benefit as well. This technology has proven to work so well that apps are already available for users to enhance audio and visual files. Need to clear a video recording of unwanted sound and background imagery? AI sound techniques and tools can speed up that editing process.
New listening experiences add another layer of useful applications that AI noise reduction technology has. The music industry, specifically in composition and production, has already integrated this audio technology. It’s been changing the dynamics of music creation, paving the way for human-AI collaborations.
Future of AI in Sound Technology
Frustration with technology only grows when it is not delivering what's promised. The potential for AI in sound technology goes beyond noise-canceling features. Future applications include making strides in the medical industry.
For example, hearing aids are going to evolve from current models. AI technology is already part of this life-changing auditory equipment. With AI noise reduction tech, users get more precise filters. The tech can make automatic adjustments as wearers travel between environments. The algorithms are designed to eliminate cross-talk and echo while simultaneously separating room sounds.
Audio engineering wasn’t limited to improving sound quality either. AI noise reduction opened the door for improvements to supportive technology like VR and AR, so much so that AR tech has gradually been introduced as a viable tool for businesses.
Summary
Noise-canceling AI and noise-reduction technology help enhance the audio experience across the table. It’s a trusted sound technology that impacts phone calls, video chats, commercial advertising, entertainment, and film. The manufacturing of audio and video equipment now includes the integration of adaptive ANC and AI noise reduction technology.
The impact AI noise reduction has had goes beyond the professional fields. It’s a common technology that’s earned a reputation in the creative industries and gaming. The artificial intelligence field is always looking to deliver fully immersive audio and video experiences. That shouldn’t come as a surprise when visual arts, music production, film, and gaming all use similar tools.
From daily use for personal communications to convenient sound clarity for work, AI noise reduction devices will slowly become the norm. It’s highly recommended that tech-savvy fans, professionals, and creatives from every industry consider exploring AI-enhanced audio solutions.
