An information processing method includes generating performance data that represent a performance of a musical piece that reflects a change caused by a factor that alters the performance of the musical piece, by inputting musical score data, which represent a musical score of the musical piece, and variability data, which represent the factor, into a trained model.

Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments. Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labelling, characterizing, or otherwise processing music.

A method of creating AI-composed music having a style that reflects and/or augments the personal style of a user includes selecting and/or composing one or more seed compositions; applying variation and/or mash-up methods to the seed compositions to create training data; training an AI using the training data; and causing the AI to compose novel musical compositions. The variation methods can include methods described in the inventor's previous patents. A novel method of creating mash-ups is described herein. Variation and mash-up methods can further be applied to the A compositions. The AI compositions, and/or variations and/or mash-ups thereof, can be added to the training data for re-training of the AI. The disclosed mash-up method includes parsing the seed compositions into sequences of elements, which can be of equal length, beat-matching the seed compositions to make corresponding elements of equal beat length, and combining the elements to form a mash-up.

The present invention relates to systems and methods for visual image audio composition. In particular, the present invention provides systems and methods for audio composition from a diversity of visual images and user determined sound database sources.

An automated music composition and generation system provided with a system user interface enabling system users to review, select and provide one or more musical experience descriptors as well as time and/or space parameters, to an automated music composition and generation engine, operably connected to the system user interface. The automated music composition and generation engine includes a musical kernel generation subsystem for automatically analyzing and saving musical kernel elements automatically abstracted from the digital piece of music. The abstracted musical kernel elements distinguish the digital piece of music from any other digital piece of music automatically composed and generated by the automated music composition and generation system, and serve as a music kernel definition of the digital piece of composed music, which can be subsequently used during future automated music composition and generation processes, and in future music production environments, to replicate the digital piece of composed music at a later time, either with complete or incomplete accuracy, as required or desired by the system user.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. It provides and benefits from a combination of expert knowledge resident in an expert engine which contains rules for a musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of fitting audio loops from a database of audio loops.

An automated music performance system that is driven by the music-theoretic state descriptors of any musical structure (e.g. a music composition or sound recording). The system can be used with next generation digital audio workstations (DAWs), virtual studio technology (VST) plugins, virtual music instrument libraries, and automated music composition and generation engines, systems and platforms. The automated music performance system generates unique digital performances of pieces of music, using virtual musical instruments created from sampled notes or sounds and/or synthesized notes or sounds. Each virtual music instrument has its own set of music-theoretic state responsive performance rules that are automatically triggered by the music theoretic state descriptors of the music composition or performance to be digitally performed. An automated virtual music instrument (VMI) library selection and performance subsystem is provided for managing the virtual musical instruments during the automated digital music performance process.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. It provides and benefits from a combination of expert knowledge resident in an expert engine which contains rules for a musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of fitting audio loops from a database of audio loops. Additionally, in some embodiments there is provided a method of energy-based song construction where the tracks of a multi-track work are balanced depending on the desired output volume level of the final project.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. In some embodiments, an embodiment is provided with a database that contains harmony templates which can be used by the user to augment the playback of a given music work. Various embodiments of the instant invention also benefit from a combination of expert knowledge resident in an expert engine which contains rules for musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of compatible audio loops from a database of audio loops.

A music composition, editing, and playback system and method provides a user interface design based on geometric interpretation of music theory replacing traditional modern music notation with geometric shapes including chords represented by polygons that are colored with colors or hues.