The winner of the startup competition “Vodafone Idea of the Year 2016” has attracted investment from Prague-based J&T Ventures, as well as prominent early customers including Siemens.
Pavel Konecny, Co-founder and CEO of Neuron Soundware, announced this week together with Adam Kocik, Managing Director of J&T Ventures, an investment of €600,000 to allow the Prague-based startup to capitalize upon early traction with its machine learning technology for heavy industry. The investment will help Neuron Soundware to ramp up its team, refine its technology, and expand its customer reach to include aerospace manufacturers, rail operators, and automotive companies.
Neuron Soundware, founded in 2016, garnered its initial investment from Prague-based Seed Accelerator StartupYard. There founding team, a group of AI experts led by Konecny, conceived of a device which can listen to heavy machinery, and over time, learn to recognize mechanical issues and predict when the machinery is likely to fail. Since attending StartupYard, they have developed a device employing high-end sensors used in aerospace, and audio processing software that can be plugged directly into heavy machinery and can warn of future mechanical problems. The company announced a cooperation with Siemens in 2016, and was invited to join the Airbus Innovation Lab the same year.
“We are continually impressed by the Neuron Soundware team’s technical prowess and ability to attack very complex problem sets with novel approaches and technology,” Kocik commented on the investment, “this technology is going to be even more essential as the IoT [Internet of Things] matures. Neuron Soundware will help to make machines safer, more efficient, and longer lasting.” The investment, a cooperation between J&T Ventures and a private investor, will be used to refine the engineering of Neuron Soundware’s physical devices and software, and to support its outreach to large industrial machinery firms, where demand for the technology is already growing.
According to Konecny, the technology, based on “deep neural networks,” learns from the sounds machinery produces, and can detect patterns too faint or complex for a human to hear, diagnosing issues with machinery well before they become catastrophic. Konecny says of the technology: “Sound is a rich source of data, and also quite universal, which is why mechanics and engineers rely on it so much. But a human cannot listen to 100 airplane or diesel engines for 1000 hours each, and make sense of it all. A machine can do this, and when one engine fails, it can apply that learning to all it has already heard, thus greatly enhancing our ability to detect and prevent future problems.”
“When Neuron Soundware joined us for our 6th program [out of 8], their approach to understanding sound had never really been tried before,” commented Cedric Maloux, CEO at StartupYard Accelerator, “leveraging StartupYard’s mentor network, locally and abroad, they were able to very quickly prove that there was a huge need for this kind of technology.” The company notes that future applications for machine learning and sound reach beyond machine maintenance, to product testing, autonomous navigation, green energy solutions, and even security. “Sound is everywhere,” remarks Konecny, “and we’ve just started to see how we can use it to understand more of how everything works.”