Embodiments of the present disclosure relate to a sensory experience providing apparatus for providing a sensory experience based on sound in a vehicle, and a method thereof. The controller is configured to receive a sound played in the vehicle, extract a sound feature from the received sound, generate sensory information based on the extracted sound feature, and provide a sensory experience based on the sensory information.

Vehicle machine learning methods include providing one or more computer processors communicatively coupled with a vehicle. Using data gathered from biometric sensors and/or vehicle sensors, a machine learning model is trained to determine a mental state of a driver and/or a driving state corresponding with a portion of a trip. In implementations the mental or driving state may be determined without a machine learning model. Based at least in part on the determined mental state and the determined driving state, one or more interventions are automatically initiated to alter the mental state of the driver. The interventions may include preparing (or modifying) and initiating a music playlist, altering a lighting condition within the vehicle, altering an audio condition within the vehicle, altering a temperature condition within the vehicle, and initiating, altering, or withholding conversation from a conversational agent. Vehicle machine learning systems perform the vehicle machine learning methods.

A method of making audio music selection and creating a mixtape, comprising importing song files from a song repository; sorting and filtering the song files based on selection criteria; and creating the mixtape from the song files sorting and filtering results. The sorting and filtering of the song files comprise: spectral analyzing each of the song files to extract low level acoustic feature parameters of the song file; from the low level acoustic feature parameter values, determining the high level acoustic feature parameters of the analyzed song file; determining a similarity score of each of the analyzed song files by comparing the acoustic feature parameter values of the analyzed song file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed song files according to their similarity scores; and filtering out the analyzed song files with first similarity scores lower than a filter threshold.

Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including: receiving media content from user equipment, wherein the media content comprises a sample of an existing work used to modify an original work; analyzing the media content using machine learning to determine a plurality of composition parameters that characterize aspects of the media content; recording the plurality of composition parameters determined, including an identification of composition parameters in the plurality of composition parameters attributable to the existing work, and an identification of composition parameters attributable to the original work, in a blockchain ledger; searching for the composition parameters in the blockchain ledger for one or more works having similar composition parameters; and reporting any works found during the searching to the user equipment.

A method and system may provide for interactive song generation. In one aspect, a computer system may present options for selecting a background track. The computer system may generate suggested lyrics based on parameters entered by the user. User interface elements allow the computer system to receive input of lyrics. As the user inputs lyrics, the computer system may update its suggestions of lyrics based on the previously input lyrics. In addition, the computer system may generate proposed melodies to go with the lyrics and the background track. The user may select from among the melodies created for each portion of lyrics. The computer system may optionally generate a computer-synthesized vocal(s) or capture a vocal track of a human voice singing the song. The background track, lyrics, melodies, and vocals may be combined to produce a complete song without requiring musical training or experience by the user.

An approach is provided in which an information handling system configures a reinforcement learning model based inspiration selections received from a user. The information handling system performs training iterations using the configured reinforcement learning model, which generates multiple actions and multiple rewards corresponding to multiple actions. The information handling system determines that the multiple rewards reach an empirical threshold and, in turn, generates a musical composition based on the multiple actions.

An artificial intelligence (AI) method includes generating a first musical interaction behavioral model. The first musical interaction behavioral model causes an interactive electronic device to perform a first set of musical operations and a first set of motional operations. The AI method further includes receiving user inputs received in response to the performance of the first set of musical operations and the first set of motional operations and determining a user learning progression level based on the user inputs. In response to determining that the user learning progression level is above a threshold, the AI method includes generating a second musical interaction behavioral model. The second musical interaction behavioral model causes the interactive electronic device to perform a second set of musical operations and a second set of motional operations. The AI method further includes performing the second set of musical operations and the second set of motional operations.

A system and method for synchronizing an audio or MIDI file with a video file are provided. The method includes receiving a first audio or MIDI file, receiving a video file, and operating an audio synchronization module to perform steps of synchronizing the first audio or MIDI file with the video file, marking an event in the video file at a point on a timeline, detecting a first musical key for the event, retrieving a musical stinger or swell from a library, in which the musical stinger or swell is a second audio or MIDI file and is tagged with a second musical key, and the second musical key is relevant to the first musical key, and placing the musical stinger or swell at the point of the timeline marked for the event.

An automated music composition and generation system having an automated music composition and generation engine for processing musical experience descriptors and space parameters selected by the system user. The engine includes: a user taste generation subsystem for automatically determining the musical tastes and preferences of each system user based on user feedback and autonomous piece analysis, and maintaining a system user profile reflecting musical tastes and preferences of each system user; and a population taste aggregation subsystem for aggregating the musical tastes and preferences of the population of system users, and modifying the musical experience descriptors and/or time and/or space parameters provided to the automated music composition and generation engine, so that the digital pieces of composed music better reflect the musical tastes and preferences of the population of system users and meet future system user requests for automated music compositions.

A method and system for analyzing audio (eg. music) tracks. A predictive model of the neuro-physiological functioning and response to sounds by one or more of the human lower cortical, limbic and subcortical regions in the brain is described. Sounds are analyzed so that appropriate sounds can be selected and played to a listener in order to stimulate and/or manipulate neuro-physiological arousal in that listener. The method and system are particularly applicable to applications harnessing a biofeedback resource.