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South Korean startup raises $2 million Series A to detect the sounds missed by speech recognition – TechCrunch

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Sit quietly for a second and take note of the totally different sounds round you. You would possibly hear home equipment beeping, vehicles honking, a canine barking, somebody sneezing. These are all noises, a Seoul-based sound recognition startup, is coaching its SaaS platform to establish. The corporate’s objective is to develop software program that may establish virtually any form of sound and be utilized in a variety of good {hardware}, together with telephones, audio system and vehicles, co-founder and chief govt Yoonchang Han informed PJDM. introduced it has raised $2 million in Sequence A funding, led by Smilegate Funding, with participation from Shinhan Capital and NAU IB Capital. This brings its complete funding to this point to $2.7 million, together with a seed spherical from Kakao Ventures, the funding arm of the South Korean web big. will use its Sequence A on hiring over the subsequent 18 months and to extend the dataset of sounds used to coach its deep studying algorithms.

The corporate was based in 2017 by a crew of six music and audio analysis scientists, together with Han, who accomplished his PhD in music data retrieval at Seoul Nationwide College. Whereas engaged on his doctorate, Han discovered “that everybody was actually specializing in speech recognition programs. There are such a lot of corporations for that, however analyzing different kinds of sounds are technically fairly totally different from speech recognition.”

Speech recognition know-how normally acknowledges one or two voices at a time, and assumes that persons are participating in a dialog, as an alternative of speaking over each other. It additionally makes use of linguistic data in post-processing to extend accuracy. However with music or environmental noises, several types of sounds normally overlap.

“We have now to take care about all totally different frequency ranges, and there are usually not solely voices, however actually 1000’s of sounds on the market,” Han stated. “So we predict this would be the subsequent technology of sound recognition, and that was the motivation for our startup.”’s SaaS, referred to as Cochl.Sense, is offered as a cloud API and edge SDK, and might presently detect about 40 totally different sounds, that are grouped into three classes: emergency detection (together with glass breaking, screaming and sirens), human interplay (which incorporates utilizing finger snaps, claps or whistles to work together with {hardware}) and human standing (to establish feels like coughing, sneezing or loud night breathing to be used instances like affected person monitoring or automated audio captioning).

Han stated the corporate additionally plans so as to add new performance to Cochl.Sense to be used in houses (together with good audio system), autos and music evaluation. Cochl.sense’s flexibility means it will possibly probably match many use instances, together with turning a sensible speaker right into a “management tower” for dwelling home equipment by detecting the noises they make, or serving to listening to impaired individuals by sending alerts about noises, like automobile horns, to wearable gadgets together with good watches.

The sound recognition panorama

Han notes that over the previous three years or so, there was a shift from specializing in speech recognition know-how to different sounds as effectively.

For instance, extra main tech corporations, like Amazon, Google and Apple, are including context-aware sound recognition to their merchandise. For instance, each Amazon Alexa Guard and Nest Safe detect the sound of glass breaking, whereas iOS 14’s sound recognition enabled it so as to add new accessibility options.

Han stated the launches by main tech corporations is a boon for, as a result of it signifies that the marketplace for sound recognition know-how is rising. The startup plans to work with many various industries, however is presently centered on good client gadgets and automotive as a result of that’s the place probably the most curiosity for its software program is coming from. For instance, is presently engaged on a challenge with Daimler AG to incorporate its sound recognition in vehicles (for instance, alerts if a baby is locked inside), along with collaborations with main digital, telecommunications and client good corporations.

Software program that may establish feels like gunshots, glass breaking and different noises for emergency detection has been round for many years, however typical know-how usually resulted in false alarms or required the usage of particular microphones and different {hardware}, Han stated.

Different corporations devoted to bettering sound recognition know-how embody Cambridge, England’s Audio Analytica, which focuses on context-based sound intelligence, and Netherlands-based Sound Intelligence, which develops software program for emergency alert and healthcare programs. plans to distinguish by constructing software program that can be utilized with a big selection of microphones, together with in low-end smartphones or USB microphones, with no need to be fine-tuned, as an alternative counting on deep studying to refine its algorithms and scale back false positives.

Through the early levels of constructing a dataset for a selected sound,’s crew data many audio samples by themselves utilizing older smartphone fashions and USB microphones, to make sure that their software program will work even with out high-quality microphones.

Different samples are gathered from on-line sources. As soon as the sound’s preliminary studying mannequin reaches a sure stage of accuracy, it’s then in a position to search on-line by itself for extra of the identical form of audio clips, exponentially rising the velocity of information coaching.’s Sequence A will allow it to construct datasets of audio samples extra rapidly, permitting it so as to add extra sounds to its software program.

“All of our co-founders are researchers on this area, so sign processing and machine studying methods–we are attempting many various algorithms, as a result of each sound has totally different traits,” stated Han. “We have now to attempt many various issues to make one single mannequin that may establish all totally different sounds.”

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Catherine Shu