Computer-based systems, devices, and methods for automatically generating aesthetic chord progressions and key modulations in musical compositions are described. Known harmonic relationships are expanded upon to produce a much richer set of harmonic transition probability models compared to conventional music theory, and these models are leveraged by a computer-based musical composition system to generate new musical compositions and variations of existing musical compositions. Techniques for enabling a computer-based musical composition system to automatically determine when to introduce a key modulation, what key to module to, and what chord progression(s) to use within the new key are all described.

There is disclosed a music yielding system including a controller, a music yielding device, a music analyzing device, and a musical data transferring device. The music yielding device may yield one or more sets of musical notes conforming to one or more attributes. The controller may cause one or more criteria to be set determining conformance of one or more of the sets of musical notes to one or more of the attributes. The music analyzing device may calculate and transmit one or more correlations within one or more of the sets of musical notes. The musical data transferring device may transfer one or more of the sets of musical notes between one or more origins and one or more destinations.

An approach is provided for converting color data into one or more musical notes. The approach involves reading, using a color-reading device, respective colors applied to a canvas by a plurality of drawing instruments as color data, wherein each of the plurality of drawing instruments is configured to draw in respective colors of a color palette, and wherein the respective colors of the color palette correspond respectively to a set of musical notes. The approach also involves processing, using a color processing module, the color data to generate a composition of the one or more musical notes from the color data based on a set of musical notes that correspond to the respective colors in the color data.

Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

Techniques are disclosed relating to implementing user-created controls to modify music content. A music generator system may be configured to automatically generate output music content by selecting and combining audio tracks based on various parameters. Users may create their own control elements that the music generator system may train (e.g., using AI techniques) to generate output music content according to a user's intended functionality of a user-created control element.

Techniques are disclosed relating to tracking contributions to composed music content. In some embodiments, a computer system determines playback data for a music content mix, where the playback data indicates characteristics of playback of the music content mix and the music content mix includes a determined combination of multiple audio tracks. In some embodiments, the system records, in an electronic block-chain ledger data structure, information specifying individual playback data for one or more of the multiple audio tracks in the music content mix. The information specifying individual playback data for an individual audio track may include usage data for the individual audio track and signature information associated with the individual audio track.

Techniques are disclosed relating to automatically generate new music content based on image representations of audio files. A computer system generate image representations of audio files. The image representations may be generated, for example, based on data in the audio files and MIDI representations of the audio files. Audio files for combination may then be selected based on analysis of the image representations. For example, image-based machine learning algorithms may be implemented to assess the image representations and select music for combining.

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).

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.

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.