A method and system are provided. The method includes analyzing, by a hardware processor, one or more images to determine cognitive characteristics related to the one or more images and to a user. The method further includes forming, by the hardware processor, a composable template relating to the cognitive characteristics. The method also includes performing, by the hardware processor or another hardware processor, an amelioration action with respect to a displaying on a display device of at least one image from the one or more images, based on the composable template. The amelioration action is selected from a set of amelioration actions that include transmitting the at least one image to the disturbing image repository.

A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. Harmonic distortion associated with at least one microphone may be determined. One or more harmonic distortion-based augmentations may be performed on the plurality of signals based upon, at least in part, the harmonic distortion associated with the at least one microphone, thus defining one or more harmonic distortion-based augmented signals.

A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. Harmonic distortion associated with at least one microphone may be determined. One or more harmonic distortion-based augmentations may be performed on the plurality of signals based upon, at least in part, the harmonic distortion associated with the at least one microphone, thus defining one or more harmonic distortion-based augmented signals.

In-ear sound pressure level, SPL, is determined that is caused by output audio being converted into sound by a headset worn by a user. The in-ear SPL is converted into a sound sample having units that are suitable for evaluating sound noise exposure. These operations are repeated to produce a sequence of sound samples during playback. This sequence of sound samples is then written to a secure database. Access to the database is authorized by the user. Other aspects are also described and claimed.

A tag estimation device capable of estimating, for an utterance made among several persons, a tag representing a result of analyzing the utterance is provided. The tag estimation device includes an utterance sequence information vector generation unit that adds a t-th utterance word feature vector and a t-th speaker vector to a (t−1)-th utterance sequence information vector u.sub.t-1 that includes an utterance word feature vector that precedes the t-th utterance word feature vector and a speaker vector that precedes the t-th speaker vector to generate a t-th utterance sequence information vector u.sub.t, where t is a natural number, and a tagging unit that determines a tag l.sub.t that represents a result of analyzing a t-th utterance from a model parameter set in advance and the t-th utterance sequence information vector u.sub.t.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

One embodiment provides a method, including: receiving, at an audio input device associated with an information handling device, an audible user query; identifying, using a processor, syntactic aspects of the audible user query; determining, based at least in part on the syntactic aspects and regardless of whether a trained answer to the audible user query is available, an answer responsive to the audible user query; and providing, using an output device associated with the information handling device, the answer. Other aspects are described and claimed.

One embodiment provides a method, including: receiving, at an audio input device associated with an information handling device, an audible user query; identifying, using a processor, syntactic aspects of the audible user query; determining, based at least in part on the syntactic aspects and regardless of whether a trained answer to the audible user query is available, an answer responsive to the audible user query; and providing, using an output device associated with the information handling device, the answer. Other aspects are described and claimed.

A system includes a memory configured to store a first acoustic profile associated with a device. The first acoustic profile includes at least two of the following acoustic characteristics: frequency, amplitude, and time. The system further includes a processor communicatively coupled to the memory and an acoustic sensor. The processor is configured to receive, from the acoustic sensor, an acoustic signal comprising one or more of the frequency and amplitude, compare the received acoustic signal to the first acoustic profile, and determine that the received acoustic signal matches the first acoustic profile based on the comparison of the received acoustic signal to the first acoustic profile.