By Ashish P. Dhakan, MD & CEO, Prama Hikvision India
By assigning significance to each and every soundwave, Artificial Intelligence (AI) is transforming ordinary audio experiences into something extraordinary. Thanks to AI, conventional conferences are becoming immersive experiences, new excitement is being injected into background music, public address systems are being optimised, and AI-driven audio is bringing meticulous accuracy into industrial inspections.
Artificial Intelligence (AI) is one of the most significant technologies of our generation. In the world of audio, AI is dramatically reshaping the way we perceive and interact with sound. It is transforming the clarity of virtual meetings and remote classrooms; it is creating new ways of monitoring mechanical and electrical equipment; it is being used to tweak the performance of cars; and it is being used to identify illegal horn honking in traffic management applications.
In this article, we look at three of the key technology trends that are emerging as AI-enhanced audio applications mature.
Trend 1: AI is improving the clarity of communications
In the bustling world of virtual interactions, AI stands at the forefront of tackling detrimental sound anomalies like echoes, howling, and background noise.
● AI can reduce echoes
Echoes decrease sound quality, hinder speech recognition, and create unclear communication, especially during audio and video calls with transmission delays. Unlike traditional echo cancellation methods, which can struggle with environmental noise and device movement, AI deep learning technology is able to dynamically update real-time echo cancellation effectiveness based on signals and environmental information.
● AI can reduce howling
Many of us can imagine the embarrassment of sudden, jarring noises interrupting a video conference. Characterised by sharp and irritating noises, ‘howling’ occurs when devices are too close to each other, causing feedback. It can be an extremely unpleasant disruption to remote meetings, hampering smooth communication and causing the loss of important information. Adaptive AI howling suppression technology and feedback algorithms solve these issues while maintaining sound quality and amplification.
● AI can reduce background noise
AI-driven noise reduction fine-tunes the audio signal, stripping away undesirable background noise and amplifying the clarity of speech. As a result, every virtual meeting and remote classroom experience becomes as clear as if it were in person.
Trend 2: AI is enhancing mechanical and electrical equipment monitoring
In many industrial settings, a common method of identifying mechanical faults is to listen to the sounds they make. An experienced electrical inspector, for example, can ‘hear’ abnormal sounds coming from a transformer. Just by listening to the sound, they can determine whether it is running with an overload or experiencing a poor internal contact.
There are obvious drawbacks to human ear detection, however. To start with, it is clearly impossible for humans to focus on fault detection 24/7. Moreover, the presence – or absence – of experience can greatly affect the success of fault detection. Additionally, the human ear struggles to capture short and abrupt sounds for detailed analysis; it requires listening to sounds for a longer period to pinpoint a problem.
AI-equipped algorithm systems, on the other hand, can easily overcome all these challenges. AI sound pattern recognition enables real-time sounds to be monitored which, in turn, can be used to determine equipment status and identify abnormal sounds. This makes it possible to create automated quality inspection solutions. AI audio detection can also identify potential risks in the electricity sector, such as anomalies in substations and power grid monitoring.
Trend 3: AI is pinpointing the origins of sounds
Sound source localisation combines arrays of microphones with beamforming (spatial filtering) technology to pinpoint the origin of sounds. In perfect conditions, it can be a very useful technique to enhance the experience of audio in various fields. However, when there is excessive background noise or multiple people talking simultaneously, traditional beamforming applications and its improved algorithms have suffered accuracy issues when struggling to pinpoint the origins of sounds.
AI-enhanced algorithms can improve this. For video conferences, this means spotlighting the active speaker amongst a sea of participants. The increased spatial accuracy also helps with the identification and location of engine noises when tweaking performance in the automotive industry.
This technology is also playing an important role in traffic management. Here, AI-powered sound source localisation can be used to monitor real-time sound signals in traffic and to identify and locate vehicles that are using their horn illegally. Used as part of an intelligent traffic management system, this automated solution reduces the need for manual patrols, improves traffic management efficiency, serves as a deterrent, and enhances overall traffic safety.
