Abstract: Broadly speaking, the present techniques generally relate to a system, computer-implemented method and apparatus for training a machine learning, ML, model to perform sound enhancement for a target user in real-time, and to a method and apparatus for using the trained ML model to perform sound enhancement of audio signals in real-time. Advantageously, the present techniques are suitable for implementation on resource-constrained devices that capture audio signals, such as smartphones and Internet of Things devices.

Abstract: A method of predicting performance of a hardware arrangement or a neural network model includes: obtaining one or more of a first hardware arrangement or a first neural network model, obtaining a first graphical model comprising a first plurality of nodes corresponding to the obtained first hardware arrangement or the obtained first neural network model, wherein each node of the first plurality of nodes corresponds to a respective component or device of the first plurality of interconnected components or devices or a respective operation of the first plurality of operations; extracting, based on the first graphical model, a first graphical representation of the obtained first hardware arrangement or the obtained first neural network model; predicting, based on the first graphical representation, performance of the obtained first hardware arrangement or the obtained first neural network model; and outputting the predicted performance.

Abstract: Among other things, one or more techniques and/or systems are provided for annotating content based upon user reaction data and/or for maintaining a searchable content repository. That is, a user may request and/or opt-in for user reaction data to be detected while a user is experiencing content (e.g., watching a movie, walking through a park, interacting with a website, participating on a phone conversation, etc.). Metadata associated with the content may be used to determine when and/or what sensors to use to detect the user reaction data (e.g., metadata specifying an emotional part of a movie). The content may be annotated with a reaction annotation corresponding to the user reaction data, which may be used to organize, search, and/or interact with the content. A search interface may allow users to search for content based upon annotation data and/or aggregated annotation data of one or more users who experienced the content.

Abstract: Among other things, one or more techniques and/or systems are provided for annotating content based upon user reaction data and/or for maintaining a searchable content repository. That is, a user may request and/or opt-in for user reaction data to be detected while a user is experiencing content (e.g., watching a movie, walking through a park, interacting with a website, participating on a phone conversation, etc.). Metadata associated with the content may be used to determine when and/or what sensors to use to detect the user reaction data (e.g., metadata specifying an emotional part of a movie). The content may be annotated with a reaction annotation corresponding to the user reaction data, which may be used to organize, search, and/or interact with the content. A search interface may allow users to search for content based upon annotation data and/or aggregated annotation data of one or more users who experienced the content.

Abstract: Among other things, one or more techniques and/or systems are provided for annotating content based upon user reaction data and/or for maintaining a searchable content repository. That is, a user may request and/or opt-in for user reaction data to be detected while a user is experiencing content (e.g., watching a movie, walking through a park, interacting with a website, participating on a phone conversation, etc.). Metadata associated with the content may be used to determine when and/or what sensors to use to detect the user reaction data (e.g., metadata specifying an emotional part of a movie). The content may be annotated with a reaction annotation corresponding to the user reaction data, which may be used to organize, search, and/or interact with the content. A search interface may allow users to search for content based upon annotation data and/or aggregated annotation data of one or more users who experienced the content.

Abstract: Application testing and analysis may include performing perturbations to affect an environment associated with the application executing on a user device without affecting other applications executing on the user device. The execution of the application may be traced while the perturbations are being performed to determine an amount of resources of the user device consumed by the application and to determine whether a performance of the application was degraded.

Abstract: Application testing and analysis may include performing perturbations to affect an environment associated with the application executing on a user device without affecting other applications executing on the user device. The execution of the application may be traced while the perturbations are being performed to determine an amount of resources of the user device consumed by the application and to determine whether a performance of the application was degraded.

Abstract: Among other things, one or more techniques and/or systems are provided for annotating content based upon user reaction data and/or for maintaining a searchable content repository. That is, a user may request and/or opt-in for user reaction data to be detected while a user is experiencing content (e.g., watching a movie, walking through a park, interacting with a website, participating on a phone conversation, etc.). Metadata associated with the content may be used to determine when and/or what sensors to use to detect the user reaction data (e.g., metadata specifying an emotional part of a movie). The content may be annotated with a reaction annotation corresponding to the user reaction data, which may be used to organize, search, and/or interact with the content. A search interface may allow users to search for content based upon annotation data and/or aggregated annotation data of one or more users who experienced the content.