#Krisp noise reduction how to
#Krisp noise reduction windows

Since we use deep learning it is important to have a powerful model training infrastructure. To capture that diversity, we trained our model with data from more than 3,000 real room environments and more than 115,000 synthetically created rooms. The characteristics of the environment from which our customers are joining their online Teams meetings has a strong impact on the speech signal as well. Another important aspect was to include emotions in our clean speech so that expressions like laughter or crying are not suppressed. For the noise data we included 150 noise types to ensure we cover diverse scenarios that our customers may run into from keyboard typing to toilet flushing or snoring. For clean speech we ensured that we had a balance of female and male speech and we collected data from 10+ languages which also include tonal languages to ensure that our model will not change the meaning of a sentence by distorting the tone of the words. Instead, we either used publicly available data or crowdsourcing to collect specific scenarios. To comply with Microsoft’s strict privacy standards, we ensured that no customer data is being collected for this data set. To achieve this dataset diversity, we have created a large dataset with approximately 760 hours of clean speech data and 180 hours of noise data.

There needs to be enough diversity in the data set in terms of the clean speech, the noise types, and the environments from which our customers are joining online meetings. The key is to train the ML model on a representative dataset to ensure it works in all situations our Teams customers are experiencing. The AI-based noise suppression relies on machine learning (ML) to learn the difference between clean speech and noise. With the increased work from home due to the COVID-19 pandemic, noises such as vacuuming, your child’s conflicting school lesson or kitchen noises have become more common but are effectively removed by our new AI-based noise suppression, exemplified in the video below. While traditional noise suppression algorithms can only address simple stationary noise sources such as a consistent fan noise, our AI-based approach learns the difference between speech and unnecessary noise and is able to suppress various non-stationary noises, such as keyboard typing or food wrapper crunching. Our new noise suppression feature works by analyzing an individual’s audio feed and uses specially trained deep neural networks to filter out noise and only retain speech.

#Krisp noise reduction how to

See this support article for details about how to turn it on and more here.

#Krisp noise reduction windows

Users can enable this helpful new feature by adjusting their device settings before their call or meeting and selecting 'High' in the 'Noise suppression' drop-down (note this feature is currently only supported in the Teams Windows desktop client). We are excited to announce that users will have the ability to remove unwelcome background noise during their calls and meetings with our new AI-based noise suppression option.

Whether it be multiple meetings occurring in a small space, children playing loudly nearby, or construction noise outside of your home office, unwanted background noise can be really distracting in Teams meetings.