Software Technology for A redundant voltage regulator for memory devices

BANK OF AMERICA CORPORATION (Charlotte, NC)

Intelligent noise cancellation technology for video and audio conference calls. Different levels of deep learning model classifiers can be used to detect, in real-time the existence of noise data and voice data within audio input signals being received at numerous audio input devices. As a result, the correct action is taken to prevent the noise information from being included in the subsequent audio communication. Specifically, a lightweight neural network-based model classifier is initially employed to detect noise data or the presence of trigger words or phrases within audio input signals. If the model is unable to detect the trigger words or phrases in the audio signals, the heavyweight neural network-based classifier is employed. The audio signals are then converted into text format to enable the user to understand the language.

The capability to use noise cancellation systems during conventional telephone calls is well-known. However, most of the existing systems for noise cancellation are not designed to work with multiple audio resources (i.e., multiple audioinputs/microphones), such as are present in a telepresence environment/room.

In a telepresence-based environment, every audio input device (i.e., microphone) is treated equally for listening to audio. In the end, the audio signal is not solely focused on the people who are talking, and any other inputs from otheraudio input devices are not removed prior to audio communication.

In order to prevent unwanted noises in the telepresence space the participants have to mute and unmute their microphones frequently. Moreover, due in part to the frequent switching of mute state to unmute state, and vice versa, participants will frequently start talking and not recognize that the microphone is in a mute state. In these instances the person who is not involved in the communicationis responsible for notifying the participant.

Therefore, it is important to design strategies, techniques and computer program products and other equipment that are able to be able to discern with precision the audio input devices that generate noise and which ones are the cause of humanspeech to allow accommodations to remove the background noise prior to audio communication. Also, it is necessary to control the volume of audio input devices to know which audio sources are generating noise and which ones are the primary source for human speech. An individual must be aware immediately if their audio input device detects human speech.

Click here to view the patent on USPTO website.

 

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