Computer-based systems, devices, and methods for generating variations of musical compositions are described. Musical compositions stored in digital media include one or more music data object(s) that encode notes. A first set of notes is characterized and a transformation is applied to replace at least one note in the first set of notes with at least one note in a second set of notes. The transformation may explore or call upon the full range of musical notes available without being constrained by conventions of musicality and harmony. For each particular note in the second set of notes that replaces a note in the first set of notes, whether the particular note is in musical harmony with other notes in the music data object is separately assessed and, if not, the particular note is adjusted to bring it into musical harmony with other notes in the music data object.

Computer-based systems, devices, and methods for generating musical note sequences are described. One or more musical composition(s) stored in digital media include one or more data object(s) that encode notes and/or note sequences. At least one note sequence is processed to form a time-ordered sequence of parallel notes, which is analyzed to determine a k-back probability transition matrix for the at least one note sequence. An attribute, such as a style, of the at least one note sequence is thus encoded and used to generate new note sequences that embody a similar attribute or style. In some implementations, the at least one note sequence may include a concatenated set of note sequences representative of a particular library of musical compositions.

Computer-based systems, devices, and methods for generating musical compositions are described. A population of musical compositions stored in digital media are each segmented to produce abridged samples. The samples are analyzed to identify parent compositions that best exhibit or evoke a particular desired quality. The parent compositions are cross-bred to generate a set of child compositions which are similarly segmented and analyzed. The child compositions that best exhibit or evoke the particular desired quality are re-cast as parent compositions from which another generation of child compositions are bred. Mutations in the form of musical variations are inserted in at least some iterations and the process is repeated until at least one child composition that satisfies some exit criterion is returned.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).

A metaverse application performs an audio rollback of a local game state by receiving user input from a user during gameplay of a virtual experience. The metaverse application renders a first game state of gameplay of the virtual experience on the user device based on the user input. The metaverse application receives information about a second game state of gameplay of the virtual experience from a server. The metaverse application determines that there is a discrepancy between the first game state and the second game state. The metaverse application determines an audio gap in the first game state where a modification to game audio is to be inserted. The metaverse application generates replacement audio, wherein a duration of the replacement audio matches a duration of the audio gap. The metaverse application renders a corrected game state on the user device that includes the replacement audio.

Computer-based systems, devices, and methods for assigning mood labels to musical compositions are described. A mood classifier is trained based on mood-labeled musically-coherent segments of musical compositions and subsequently applied to automatically assign mood labels to musically-coherent segments of musical compositions. In both cases, the musically-coherent segments are generated using automated segmentation algorithms.

A computer implemented method is provided for generating a prediction of a next musical note by a computer having at least a processor and a memory. A computer processor system is also provided for generating a prediction of a next musical note. The method includes storing sequential musical notes in the memory. The method further includes dividing, by the processor, the sequential musical notes into sections of a given length based on a Generative Theory of Tonal Music. The method also includes generating, by the processor, the prediction of the next musical note based upon a music model, the sections, and the sequential musical notes stored in the memory. The given length is determined based on one or more conditions.

A music composition and training system includes: identifying a first chord of a chord progression. Then, for multiple iterations, the system includes selecting a chord of the chord progression, beginning with the identified first chord, and: identifying one or more potential subsequent chords in such a way that each of the potential subsequent chords provide a musical progression; applying weighted criteria to each of the potential subsequent chords; selecting a second chord based on the weighting while also providing variety and unpredictability; and combining the second chord into the chord progression. The system then uses the generated chord progression to generate or teach music.

A music composition and training system includes: identifying a first chord of a chord progression. Then, for multiple iterations, the system includes selecting a chord of the chord progression, beginning with the identified first chord, and: identifying one or more potential subsequent chords in such a way that each of the potential subsequent chords provide a musical progression; applying weighted criteria to each of the potential subsequent chords; selecting a second chord based on the weighting while also providing variety and unpredictability; and combining the second chord into the chord progression. The system then uses the generated chord progression to generate or teach music.

Disclosed herein is a method for facilitating creating of music in real time by multiple users, in accordance with some embodiments. Accordingly, the method comprises receiving first music segment selections from a first user device, receiving second music segment selections from a second user device, transmitting the first music segment selections to the second user device, and transmitting the second music segment selections to the first user device. Further, each of the first user device and the second user device is configured for retrieving the first music segments, retrieving the second music segments, determining a universal time, synchronizing the second music segments with the first music segments to a common musical beat based on the universal time, mixing the second music segments with the first music segments based on the synchronizing, and generating a mixed music comprising the first music segments and the second music segments based on the mixing.