Abstract: Joint noise and echo suppression may be performed for enhancing two-way audio communications. Audio data is captured at a communication device and audio data transmitted to the communication device from another communication device are used as input features to a trained machine learning model that uses the transmitted audio data as a reference signal to eliminate residual echo in the captured audio data when also suppressing noise in the captured audio data.

Abstract: Techniques for the generation of dubbed audio for an audio/video are described.

Abstract: Post-filtering may be performed for ratio masks as part of audio enhancement. Audio data may be received. A machine learning model may be applied to generate gain values for different spectrum bands of the audio data. The gain values may then be modified using an envelope post-filter according to a monotonically increasing function applied to the gain values to produce modified gain values used to generate an enhanced version of the audio data.

Abstract: Systems, methods, and apparatuses for providing an estimation of remaining useful life are described. In some examples, a method for providing an estimation of remaining useful life includes receiving a request to determine a remaining useful life of a managed device before maintenance using a trained machine learning model; receiving sensor data from the managed device; applying the trained machine learning model to the received information to generate a prediction of a time to failure of the managed device and a confidence interval for that prediction; and providing the prediction of a time to failure and confidence interval for that prediction to a requester.

Abstract: Systems, methods, and apparatuses for providing a device health service are described. In some examples, a method includes receiving a request to perform a model transfer to generate a model to use on previously unseen data; receiving previously unseen data; determining a previously seen feature data most closely resembles the received previously unseen data; mapping, using the determined previously seen feature, the previously unseen data to labels; training a model using the mapped labels and the previously unseen data; and performing inference using the trained model.

Abstract: Systems, methods, and apparatuses for detecting anomalies using clusters are described. In some examples, a method includes receiving a request to perform anomaly detection using a plurality of clusters; receiving a data point; determining when the received data point is a part of one of the plurality of clusters utilizing a distance to centers of the one or more clusters, wherein: when the received data point is determined to belong to a normal cluster, assigning the received data point to the determined cluster, updating the cluster, and updating a history for the cluster, when the received data point is determined to belong to an anomalous cluster, raising an anomaly, updating the cluster, and updating a history for the cluster, and when the received data point is determined to not belong to any cluster, raising an anomaly.

Abstract: Systems, methods, and apparatuses for selecting a model are described. In some examples, a method of selecting a model includes receiving a request to perform model selection; evaluating a plurality of models to select model by: generating a plurality of metrics for each of the trained plurality of models, the plurality of metrics including at least two of an forewarning time metric of how much in advance of a failure an alert can be raised by the model, event recall metric of how many failure events were alerted to in advance of failure, an event precision metric of a ratio of true and false positives, and an area under a receiver operating characteristic (ROC) curve, calculating, for each of the trained plurality of models, a weighted harmonic mean from the at least two metrics, and selecting one of plurality of models based on the calculated weighted harmonic means; and generating and providing a report regarding the selected trained model.