Hearing The World Through Machine Learning

Hearing The World Through Machine Learning

Hearing The World Through Machine Learning

Machine learning (ML) has quietly become part of our everyday lives, powering our cars, the operations in our hospitals, and the food we eat. A number of the most fascinating businesses employing ML don’t call themselves”ML” companies.

The automobile is the clearest case, as $10 billion of funding poured into the sector in 2019 alone. As we look ahead to another generation of verticals where ML is going to have a revolutionary impact, health care stands out as one of the very promising. In healthcare, there are now remarkable methods that ML can enhance the quality of our hearing loss.

A Centuries-Old Problem

The quietest sound you can hear starts to deteriorate as you get older, but the loudest audible sound remains constant throughout your entire life. Hearing aids traditionally work by amplifying sounds with just very rough alterations, from the clinking plates at a restaurant to the noise of your youngster’s voice. They can be incredibly painful to wear, often drowning out the sounds you wish to listen to sounds you do not and this is very outstanding in noisy public places.

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You will find still an estimated48 million Americans experiencing some level of hearing loss. Despite the incidence of the issue, the predominant hearing aids available on the market today were built 50-100 years ago. Sonova is a US$17 billion Switzerland-based company that was founded in 1947, the same year that the transistor was invented. Demant is an US$8 billion Denmark-based company that has been set up in 1904, predating even the Model T. A handful of new entrants lately emerged, offering cheaper, direct-to-consumer products, but attaining quality at par with business incumbents has remained elusive.

With ML, companies can apply cutting-edge technologies to alter an age-old problem. Startups are Implementing deep learning and innovative signal processing at a granularity not before possible to enhance hearing caliber

Some incumbent hearing aid companies have recently touted their capacity to include”AI” features such as Alexa integrations and measure counters. Unfortunately, these features do not appear to improve actual hearing quality nor take advantage of true ML capacities beyond generating marketing buzz.

By comparison, startups are constructing software-based solutions that could always ship new features and upgrades to existing customers, much like the systems pioneered at Apple and Tesla, resulting in a product that keeps getting better over time. The algorithms that power these hearing aids may detect, predict, and suppress undesirable background noise, forming the basis of a non-trivial technical moat supporting the item.

Engineers have spent years building such neural network models by taking structured and unstructured data, augmenting it with in-house information (representing a range of ages, languages, and voice kinds ), feeding the information into neural network training, and then refining the predictions – a process which continuously improves the efficiency of the goods.

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In contrast, ML includes a natural flywheel effect where apparatus collect data at scale, ML training protocols automatically process the data, update themselves, and redeploy. The effect is a significant decrease in product comments cycles and an increase in the assortment of capacities available. The beauty of the strategy is that the inherent intelligence improves over time as the neural nets go through iterative training.”

Time to Invest is Now

There are many reasons to be optimistic regarding the worth ML can make to improve our health in the years to come. I strongly feel that one of the next generation of businesses where ML will also have a revolutionary impact, health care is one the most promising. The potential for hearing, primary care, clinical trials, and many other critical parts of the healthcare mystery will depend on intelligent systems. Exciting startups to emerge from the hearing aid market are only 1 instance of a new wave of ML-driven innovation in healthcare.

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