Abstract: An intelligent noise cancellation process for audio or video conference calls. Different levels of deep learning model classifiers are leveraged to determine, in real-time, the presence of noise data and voice data in audio input signals being received at numerous audio input devices. In response, appropriate action is taken to prevent the noise data from being included in the subsequent audio communication. Specifically, a lightweight neural network-based model classifier is initially used to identify noise data and/or the presence of predetermined trigger words or phrases in audio input signals. In the event that the lightweight model is unable to identify the presence of the triggering words/phrases, a heavyweight neural network-based model classifier is called upon, whereby the audio signals are attempted to be converted to a human-understandable language format (i.e., a text format) as a means of positively identifying voice data in audio input signals.

Abstract: An intelligent noise cancellation process for audio or video conference calls. Different levels of deep learning model classifiers are leveraged to determine, in real-time, the presence of noise data and voice data in audio input signals being received at numerous audio input devices. In response, appropriate action is taken to prevent the noise data from being included in the subsequent audio communication. Specifically, a lightweight neural network-based model classifier is initially used to identify noise data and/or the presence of predetermined trigger words or phrases in audio input signals. In the event that the lightweight model is unable to identify the presence of the triggering words/phrases, a heavyweight neural network-based model classifier is called upon, whereby the audio signals are attempted to be converted to a human-understandable language format (i.e., a text format) as a means of positively identifying voice data in audio input signals.