A computing device comprising a processor, the processor configured to: receive, from an image capture system, an image captured in an environment and image metadata associated with the image, the image metadata comprising an image capture time; receive a sound recognition message from a sound recognition module, the sound recognition message comprising (i) a sound recognition identifier indicating a target sound or scene that has been recognised based on captured audio data captured in the environment, and (ii) time information associated with the sound recognition identifier; detect that the target sound or scene occurred at a time that the image was captured based on the image metadata and the time information in the sound recognition message; and output a camera control command to said image capture system based on said detection.

A system and method for learning user preferences operates by posing topics in a manner similar to a human-to-human conversation. The system learns which topics to present to a human user from an initially seeded response database containing natural language phrases. The system then records user responses into the same response database or a connected response database. The system assigns user responses into categories, such as positive, negative, request for information, null, and potentially others. The system then bases future topics on what it learns during the interaction, including user responses, user response categories, time of data, location, how busy the human user typically is at difference times of day or certain days, and the like.

A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

The system comprises an input device for collecting sound and sound information or extracting sound information from a music sample; a pre-processor for pre-processing the informational collection to generate an input information test set for a characterization model, wherein the pre-processor utilizes fine-grained division and different techniques to preprocess the example informational collection; a central processor for combining sound feeling data and further developing arrangement speed, such that review makes fine-grained division for genuine music informational collection and results the inclination results by casting a ballot direction, which is configured to promote precision of music feeling grouping; a vocal division device for dividing vocal of the complicated structure of genuine music sound, and voice and foundation sound are incorporated together; and a reviewing device for reviewing the vocal detachment of music and reviewing the grouping impact of vocal and foundation sound individually, which incredibly builds the convergence of sound elements.

The system comprises an input device for collecting sound and sound information or extracting sound information from a music sample; a pre-processor for pre-processing the informational collection to generate an input information test set for a characterization model, wherein the pre-processor utilizes fine-grained division and different techniques to preprocess the example informational collection; a central processor for combining sound feeling data and further developing arrangement speed, such that review makes fine-grained division for genuine music informational collection and results the inclination results by casting a ballot direction, which is configured to promote precision of music feeling grouping; a vocal division device for dividing vocal of the complicated structure of genuine music sound, and voice and foundation sound are incorporated together; and a reviewing device for reviewing the vocal detachment of music and reviewing the grouping impact of vocal and foundation sound individually, which incredibly builds the convergence of sound elements.

Custom alerts may be generated based on sound type indicators determined using a machine learning classification model trained on user-provided sound recordings and user-defined sound type indicators. A device may provide a sound recording and a type indicator identifying an entity that made a sound in the recording for storage in a database that includes a plurality sound recordings associated with a plurality of type indicators. A machine learning classification model may be trained based on the stored recordings, including the user-defined recordings. The model may be used to classify sounds recorded by other devices and generate alerts identifying the type of sound. Thus, multiple users may contribute data to customize machine learning models that recognize sounds and generate alerts based on user-defined identifiers.

The information processing device 1X mainly includes a pair determination means 15X and a relevance degree calculation unit 16X. The pair determination means 15X is configured to determine a pair of data at least one member of which is a first digest candidate that is a candidate of a digest, the data including at least one of video data or audio data. The relevance degree calculation means 16X is configured to calculate a degree of relevance indicating a degree of probability that the pair determined by the pair determination means 15X is included in the digest at a time.

The information processing device 1X mainly includes a pair determination means 15X and a relevance degree calculation unit 16X. The pair determination means 15X is configured to determine a pair of data at least one member of which is a first digest candidate that is a candidate of a digest, the data including at least one of video data or audio data. The relevance degree calculation means 16X is configured to calculate a degree of relevance indicating a degree of probability that the pair determined by the pair determination means 15X is included in the digest at a time.

A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.

A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.