Systems, devices, and methods for encoding the harmonic structure of a musical composition in a digital data structure are described. Tonal and rhythmic commonalities are identified across the musical bars that make up a musical composition. Individual bars of the musical composition are each analyzed to characterize their respective harmonic fingerprints in various forms, and the respective harmonic fingerprints are compared to sort the musical bars into harmonic equivalence categories. Isomorphic mappings between hierarchical data structures that encode the musical composition based on musicality and harmony, respectively, are also described. The systems, devices, and methods for encoding the harmonic structure of a musical composition in a digital data structure have broad applicability in computer-based composition and variation of music.

An apparatus, method, and computer-readable storage medium that generate at least a cue point in a musical piece. The method includes generating a beat grid representing the musical piece, determining values for the beat grid, the values corresponding to an audio feature of the musical piece, and each value representing an entire duration of each beat in the beat grid of the musical piece, calculating a score for the audio feature at each of a plurality of positions in the beat grid of the musical piece, using some or all of the determined values, and generating the cue point at a particular position of the plurality of positions, based on the calculated scores.

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 method of determining on a computer-based system at least one representative segment of a musical composition, the method including providing a digital audio signal representing said musical composition; dividing said digital audio signal into a plurality of frames of equal frame duration; calculating at least one audio feature value for each frame by analyzing the digital audio signal, said audio feature being a numerical representation of a musical characteristic of said digital audio signal, with a numerical value equal to or higher than zero; identifying at least one representative frame corresponding to a maximum value of said audio feature; and determining at least one representative segment of the digital audio signal with a predefined segment duration, the starting point of said at least one representative segment being a representative frame.

A method, apparatus and system that enables a user to find and act-upon a sound-containing composition, in a group of compositions. One or more sound-segments, which are intended to prompt a user's memory, may be associated with each composition in a group of compositions. A recognition sound-segment may include a portion of its associated composition, which is more recognizable to users than the beginning part of its associated composition. A recognition-segment may contain one or more highly recognizable portion(s) of a composition. When the user is trying to locate or select a particular composition, the recognition-segments are navigated and played-back to the user, based upon a user-device context/mode. When a user recognizes the desired composition from its recognition-segment, the user may initiate a control action to playback; arrange; and/or act-upon, the composition that is associated with the currently playing recognition-segment.

A music analysis method includes estimating a plurality of provisional points that are candidates for a specific point that has musical meaning in a musical piece from an audio signal of the musical piece by using a first process, selecting a part of a plurality of candidate points, which include the plurality of provisional points and a plurality of division points that divide intervals between the plurality of provisional points, as a plurality of selection points, and estimating a plurality of specific points in the musical piece from a result of calculating a probability that each of the plurality of selection points is the specific point by using a second process which is different from the first process.

Methods, apparatus, and systems are disclosed to segment audio and determine audio segment similarities. An example apparatus includes at least one memory storing instructions and processor circuitry to execute instructions to at least select an anchor index beat of digital audio, identify a first segment of the digital audio based on the anchor index beat to analyze, the first segment having at least two beats and a respective center beat, concatenate time-frequency data of the at least two beats and the respective center beat to form a matrix of the first segment, generate a first deep feature based on the first segment, the first deep feature indicative of a descriptor of the digital audio, and train internal coefficients to classify the first deep feature as similar to a second deep feature based on the descriptor of the first deep feature and a descriptor of a second deep feature.

Visual effects, including augmented reality-type visual effects, are applied to audiovisual performances with differing visual effects and/or parameterizations thereof applied in correspondence with computationally determined audio features or elements of musical structure coded in temporally-synchronized tracks or computationally determined therefrom. Segmentation techniques applied to one or more audio tracks (e.g., vocal or backing tracks) are used to compute some of the components of the musical structure. In some cases, applied visual effects are based on an audio feature computationally extracted from a captured audiovisual performance or from an audio track temporally-synchronized therewith.

According to an embodiment, there is provided a system and method for automatic AI controlled loop based song construction. It provides and benefits from a machine learning AI in a audio loop selection engine for the generation of a song structure and for the selection of fitting audio loops from a database of audio loops. In one embodiment, the instant method provides a music generation process that utilizes an AI system that has been trained and validated on a music item database to complete the creation of a music item given an incomplete song that was started but not finished by a user.

The present disclosure relates to a method and device for audio generation. The method includes: obtaining a target rhythm, a target verse melody and a target chorus melody; configuring the target rhythm as a first audio track, the target verse melody as a second audio track, and the target chorus melody as a third audio track; generating a target audio by aligning start playing time of the first audio track, the second audio track and the third audio track to beat occurrence time of a first beat, a second beat and a third beat in a first metronome data respectively.