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.

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.

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.

Various embodiments provide techniques for generating real-time musical accompaniment for musical content included in an audio signal. A real-time musical accompaniment system receives the audio signal via an audio input device. The system extract, from the audio signal, musical information characterizing at least a portion of the musical content. The system generates musical information that has at least one of a rhythmic relationship and a harmonic relationship with the musical information. The system generates an output audio signal that is complementary to the musical information. The system transmits, substantially immediately after receiving the audio signal, the output audio signal to an audio output device.

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.

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 media composition application, such as a musical scorewriter or a digital audio workstation, provides in situ suggestions for continuation or completion of a media composition. The suggestions are based on some or all of the portion of the composition already composed or are based on a corpus of compositions, such as those by a particular composer or those of a specific genre. The length of the suggestions is specified by the user. The suggestions are provided within a graphical user interface of the application and displayed as a possible direct continuation of the composition within a musical stave. If the user rejects the suggestion, additional suggestions are automatically displayed in situ. Reductive, most-probable suggestions may be offered as well as exploratory suggestions that facilitate a creative compositional interaction between user and application. Data filters enable selected aspects of a data source to be used for suggestion generation.

A method and system for generating music uses artificial intelligence to analyze existing musical compositions and then creates a musical composition that deviates from the learned styles. Known musical compositions created by humans are presented in digitized form along with a style designator to a computer for analysis, including recognition of musical elements and association of particular styles. A music generator generates a draft musical composition for similar analysis by the computer. The computer ranks such draft musical composition for correlation with known musical elements and known styles. The music generator modifies the draft musical composition using an iterative process until the resulting musical composition is recognizable as music but is distinctive in style.

A method and system for determining a compact semantic representation of a digital audio signal using a computer-based system by calculating at least one low-level feature matrix from the digital audio signal; processing the low-level feature matrix or matrices using pre-trained machine learning engines including an ensemble of modules, wherein each module in the ensemble is trained to predict a one of a plurality of high-level feature values; and concatenating the obtained plurality of high-level feature values into a descriptor vector. The calculated descriptor vectors can be used alone, or in an arbitrary or temporally ordered combination with further descriptor vectors calculated from different audio signals extracted from the same music track, as a compact semantic representation of the respective music track.