Abstract: A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Abstract: A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Abstract: A system may include a first acoustic event detection (AED) component configured to detect a predetermined set of acoustic events, and include a second AED component configured to detect custom acoustic events that a user configures a device to detect. The first and second AED components are configured to perform task-specific processing, and may receive as input the same acoustic feature data corresponding to audio data that potentially represents occurrence of one or more events. Based on processing by the first and second AED components, a device may output data indicating that one or more acoustic events occurred, where the acoustic events may be a predetermined acoustic event and/or a custom acoustic event.

Abstract: A system may include an acoustic event detection component for detecting acoustic events, which may be non-speech sounds. Upon detection of a command to detect a new sound, a device may prompt a user to cause occurrence of the sound one or more times. The acoustic event detection component may then be reconfigured, using audio data corresponding to the occurrences, to detect future occurrences of the event.

Abstract: Described herein is a system for media presence detection in audio. The system analyzes audio data to recognize whether a given audio segment contains sounds from a media source as a way of differentiating recorded media source sounds from other live sounds. In exemplary embodiments, the system includes a hierarchical model architecture for processing audio data segments, where individual audio data segments are processed by a trained machine learning model operating locally, and another trained machine learning model provides historical and contextual information to determine a score indicating the likelihood that the audio data segment contains sounds from a media source.

Abstract: An audio event detection system that processes audio data into audio feature data and processes the audio feature data using pre-configured candidate interval lengths to identify top candidate regions of the feature data that may include an audio event. The feature data from the top candidate regions are then scored by a classifier, where the score indicates a likelihood that the candidate region corresponds to a desired audio event. The scores are compared to a threshold, and if the threshold is satisfied, the top scoring candidate region is determined to include an audio event.

Abstract: System and method for an active learning system including a sensor obtains data from a scene including a set of images having objects. A memory to store active learning data including an object detector trained for detecting objects in images. A processor in communication with the memory, is configured to detect a semantic class and a location of at least one object in an image selected from the set of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image (classification) and an uncertainty of the object detector about the location of the object in the image (localization). Using an output interface or a display type device, in communication with the processor, to display the image for human labeling when the detection metric is above a threshold.

Abstract: An image processing system includes a memory to store a classifier and a set of labeled images for training the classifier, wherein each labeled image is labeled as either a positive image that includes an object of a specific type or a negative image that does not include the object of the specific type, wherein the set of labeled images has a first ratio of the positive images to the negative images. The system includes an input interface to receive a set of input images, a processor to determine a second ratio of the positive images, to classify the input images into positive and negative images to produce a set of classified images, and to select a subset of the classified images having the second ratio of the positive images to the negative images, and an output interface to render the subset of the input images for labeling.

Abstract: An audio event detection system that subsamples input audio data using a series of recurrent neural networks to create data of a coarser time scale than the audio data. Data frames corresponding to the coarser time scale may then be upsampled to data frames that match the finer time scale of the original audio data frames. The resulting data frames are then scored with a classifier to determine a likelihood that the individual frames correspond to an audio event. Each frame is then weighted by its score and a composite weighted frame is created by summing the weighted frames and dividing by the cumulative score. The composite weighted frame is then scored by the classifier. The resulting score is taken as an overall score indicating a likelihood that the input audio data includes an audio event.

Abstract: System and method for an active learning system including a sensor obtains data from a scene including a set of images having objects. A memory to store active learning data including an object detector trained for detecting objects in images. A processor in communication with the memory, is configured to detect a semantic class and a location of at least one object in an image selected from the set of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image (classification) and an uncertainty of the object detector about the location of the object in the image (localization). Using an output interface or a display type device, in communication with the processor, to display the image for human labeling when the detection metric is above a threshold.

Abstract: An image processing system includes a memory to store a classifier and a set of labeled images for training the classifier, wherein each labeled image is labeled as either a positive image that includes an object of a specific type or a negative image that does not include the object of the specific type, wherein the set of labeled images has a first ratio of the positive images to the negative images. The system includes an input interface to receive a set of input images, a processor to determine a second ratio of the positive images, to classify the input images into positive and negative images to produce a set of classified images, and to select a subset of the classified images having the second ratio of the positive images to the negative images, and an output interface to render the subset of the input images for labeling.

Abstract: A method for training a neuron network using a processor in communication with a memory includes determining features of a signal using the neuron network, determining an uncertainty measure of the features for classifying the signal, reconstructing the signal from the features using a decoder neuron network to produce a reconstructed signal, comparing the reconstructed signal with the signal to produce a reconstruction error, combining the uncertainty measure with the reconstruction error to produce a rank of the signal for a necessity of a manual labeling, labeling the signal according to the rank to produce the labeled signal; and training the neuron network and the decoder neuron network using the labeled signal.

Abstract: The present invention is related to a social system and process used for bringing virtual social network into real life, which is allowed for gathering and analyzing a social message of at least one interlocutor from virtual social network so as to generate at least one recommended topic, allowing a user to talk with the interlocutor through the utilization of the recommended topic, and then capturing and analyzing at least one speech and behavior and/or physiological response of the interlocutor during talking so as to generate an emotion state of the interlocutor. The user is allowed to determine whether the interlocutor is interested in the recommended topic through the emotion state of interlocutor. Thus, it is possible to bring the social message on virtual network into real life, so as to increase communication topics between persons in real life.

Abstract: The present invention is related to a social system and process used for bringing virtual social network into real life, which is allowed for gathering and analyzing a social message of at least one interlocutor from virtual social network so as to generate at least one recommended topic, allowing a user to talk with the interlocutor through the utilization of the recommended topic, and then capturing and analyzing at least one speech and behavior and/or physiological response of the interlocutor during talking so as to generate an emotion state of the interlocutor. The user is allowed to determine whether the interlocutor is interested in the recommended topic through the emotion state of interlocutor. Thus, it is possible to bring the social message on virtual network into real life, so as to increase communication topics between persons in real life.