The ŠKODA AUTO After Sales department and ŠKODA AUTO DigiLab in Europe are currently trialling a new smartphone app called the “Sound Analyser” which uses artificial intelligence to help quickly and accurately identify servicing needs through sound.

The program records noises made vehicles whilst running and compares them with stored sound patterns. In the event of any discrepancies, the app uses an algorithm to determine what they are and how they can be resolved. In this way, ŠKODA says the “Sound Analyser” helps to make vehicle maintenance more efficient, reduce the time a car spends at the garage and achieve higher levels of customer satisfaction.

Stanislav Pekař, Head of After Sales at ŠKODA AUTO, said, “Sound Analyser is a prime example of the new opportunities digitalisation at ŠKODA can create, even in terms of after sales.”

Although the program is highly complex, ŠKODA sys its operation is intuitive. The sounds made whilst the vehicle is running can be recorded using a smartphone and an algorithm then compares the recording with stored sound patterns and, based on this, provides a concrete description of the result. The software is already able to recognise ten patterns, with an accuracy of over 90%, including for components such as the steering system, the air conditioning compressor, and the clutches in the direct-shift gearbox (DSG).

Neural network algorithms provide the technical basis for “Sound Analyser”. The app first converts the audio file into a spectrogram that visually depicts the acoustic signals. Using artificial intelligence, this representation is then compared with the stored recordings to identify deviations. To this end, the app categorises the potential need for upcoming maintenance or repairs based on predetermined patterns.

The smartphone app has been trialled in 14 countries – including Germany, Russia, Austria and France since June 2019. A total of 245 ŠKODA dealers have been taking part in the pilot project. They are instrumental in providing the audio recordings for the software’s ‘learning process’ and are thus directly contributing to the program’s development.