A beat timing generation device including a generation unit for generating, from data of music that has been input, information of timings on which a beat of the music depends and a plurality of pieces of intensity data indicating a power at the timings; a calculation unit for calculating a period and a phase of the beat of the music using the plurality of pieces of intensity data; and a detection unit for detecting a beat generation timing on a basis of the period and the phase of the beat.

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.

An information processing device, a tempo detection device and a video processing system are provided. A beat of a piece of performed music is detected from a musical viewpoint. The information processing device includes: an acquisition part that acquires samples of musical sound signals in a time series; an evaluation part that has an adaptive filter using the acquired samples of the musical sound signals as reference signals and using samples of musical sound signals acquired a predetermined time earlier than the samples of the musical sound signals as input signals; and a tempo determination part that sequentially inputs the samples of the musical sound signals to the adaptive filter and determines a tempo corresponding to a musical sound based on a filter coefficient when a value of the filter coefficient of the adaptive filter converges.

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.

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.