Methods and apparatus for providing metrics for the quality, attributes, and relationships of music including AI-generated music. Music classification and visualization methods are described that involve transforming music files into graphical representations, generating a similarity matrix for the music files using structural similarity techniques, and generating visualizations of the relationships among the music files using multidimensional scaling techniques. Qualitative scoring methods for AI-generated music are described that involve classifying the AI-generated music using a multi-genre classifier, generating a similarity metric for the AI-generated music to other genres using structural similarity techniques and multidimensional scaling techniques, and generating a qualitative score for the music using confidence in the classification in combination with the similarity metric.

A computer-implemented method of digital music composition that creates a digital multi-genre musical composition track by downloading a host digital music track of a first genre and two or more separate donor multi-genre musical tracks, and then selectively modulating the instruments and rhythmic patterns of the donor musical tracks by manipulating the rhythmic patterns. The manipulation includes manipulating at least one of the intensities, frequency, sound, beat, and rhythm of the rhythmic pattern. The manipulated donor musical tracks are then integrated into the host musical track to create a combined digital multi-genre musical composition track, which can be downloaded, saved in a file, and replayed as needed.

Systems and methods for generating a graphical representation of audio-file playback during playback manipulation are provided. The system may include a processor that performs a method including displaying a waveform during audio-file playback by scrolling the waveform from a right to a left portion of a display. The method includes receiving a command to manipulate the audio-file playback and displaying a first half of the waveform corresponding to the manipulated audio-file playback until a command is received to resume the audio-file playback. The first half of the waveform is a portion of the waveform adjacent to a horizontal or vertical axis. The method includes simultaneously displaying a second half of the waveform and displaying the first half of the waveform corresponding to the manipulated audio-file playback. The second half of the waveform is on an opposite side of the axis from the first half of the waveform.

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.

A 128-note MIDI-range monophonic musical keyboard instrument (100) includes an octave keypad (106) with eleven keys arranged in an analog clock face format for octave selection with the thumb of one hand, a pitch keypad (108) with twelve pitch keys similarly disposed in a clockface arrangement around a central omnivalent thirteenth key (128), enabling the nondisjointed sounding of nonadjacent notes with the thumb of the other hand. Spatial manipulation of the device, such as tilting and jabbing, can switch octaves and activate other functions, enabling one-handed operation and overcoming small-screen space limitation. Aside from producing typical electronic piano or synthesizer sounds, the device can sing in human voice an extended monosyllabic solfege covering all twelve pitch families of the common chromatic 12-tone even-tempered scale. A pictograph-based music notation (156) mirrors the circular geometry of the pitch and octave keyboards and facilitates the intuitive reading and playing of a melody.

A method and system for visualizing music using a perceptually conformal mapping system are provided. A music source file is input into a processor configured to carry out a series of steps on audio cues identified within the music and ultimately generate a simultaneous visual representation on a display device. The series of steps include application of one or more perceptually conformal mapping systems that essentially induce a synesthetic experience in which a person can experience music both acoustically and visually at the same time. The device extracts cues from the music that are designed to specifically capture fundamentals of human appreciation, maps them into visual cues, then presents those visual cues synchronized with the source music.

An information processing method is realized by a computer and includes generating a first characteristic transition which is a transition of acoustic characteristics, in accordance with an instruction from a user, generating a second characteristic transition which is a transition of acoustic characteristics of voice that is pronounced in a specific pronunciation style selected from a plurality of pronunciation styles, and generating a combined characteristic transition which is a transition of the acoustic characteristics of synthesized voice by combining the first characteristic transition and the second characteristic transition.

Systems and methods for generating a visual color display of audio-file data are provided. The system includes a processor that performs a method including receiving audio-file data; generating filtered-audio data by processing the audio-file data by frequency-band filters. The frequency band filters have different frequency bands. The method includes generating one or more waveforms corresponding to the filtered-audio data and displaying the waveforms superimposed in unique color relative to one another. The method includes downsampling the waveforms. The method includes processing the waveforms through an envelope detector. The method includes processing the waveforms through an expander and applying a gain factor. The waveforms have transparency levels at sections that are proportional or inversely proportional to amplitudes at the sections.

An apparatus, method, and computer-readable storage medium that generate a harmonized musical piece. The method includes receiving a chord selection including a musical key and a scale selection, generating, within a digital audio work session, a chord progression sequence based on the received chord selection, in response to a detected chord selection change, modifying the chord progression sequence to include a chord progression corresponding to the chord selection change, setting the chord progression sequence as a master sequence, in response to detecting a second progression sequence within the digital audio work session, transmitting an identifier to the second progression sequence setting it as a slave sequence, and establishing a synchronized communication link between the master and the slave sequences such that changes made in the master sequence are automatically effectuated in the slave sequence, and combining the master sequence and the slave sequence to form a composed musical piece.

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.