A method implemented by a determination engine is provided. The determination engine receives a media dataset comprising target piece music information, target piece audience information, corpus music information, corpus audience information, and corpus preference data. The determination engine determines a subset of the corpus music and preference information and determines at least one surprise factor of the subset of the corpus music and preference information across features at one of a plurality of orders. The determination engine learns a model that estimates a likelihood that time-varying surprise trends across the features achieves a preference level. The determination engine determines at least one surprise factor of the target piece music information across the features at the one of the plurality of orders and predicts, using the model, preference information using the time-varying surprise trends for the target piece music information across the features.

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.

A media-playback device acquires a heart rate, selects a song with a first tempo, and initiates playback of the song. The song meets a set of qualification criteria and the first tempo is based on the heart rate, such as being equal to or less than the heart rate. The media-playback device also initiates playback of a binaural beat at a first frequency. Over a period of time, the binaural beat's first frequency is changed to a second frequency. Over the period of time, the first tempo can also be changed to a second tempo, where the second tempo is slower than the first tempo.

Implementations of the disclosure describe systems and methods that leverage machine learning to automate the process of creating music and effects mixes from original sound mixes including domestic dialogue. In some implementations, a method includes: receiving a sound mix including human dialogue; extracting metadata from the sound mix, where the extracted metadata categorizes the sound mix; extracting content feature data from the sound mix, the extracted content feature data including an identification of the human dialogue and instances or times the human dialogue occurs within the sound mix; automatically calculating, with a trained model, content feature data of a music and effects (M&E) sound mix using at least the extracted metadata and the extracted content feature data of the sound mix; and deriving the M&E sound mix using at least the calculated content feature data.

An autonomous music composition and performance system employing an automated music composition and generation engine configured to receive musical signals from a set of a real or synthetic musical instruments being played by a group of human musicians. The system buffers and analyzes musical signals from the set of real or synthetic musical instruments, composes and generates music in real-time that augments the music being played by the band of musicians, and/or composes and generates music for subsequent playback, review and consideration by the human musicians.

An automated music composition and generation system includes a graphical user interface (GUI) based system user interface for enabling system users to review and select one or more musical experience descriptors as well as time and/or space parameters; and an automated music composition and generation engine, operably connected to the GUI-based system user interface, for receiving, storing and processing the musical experience descriptors and time and/or space parameters, and composing and generating digital pieces of music, each containing a set of musical notes arranged and performed in the digital piece of composed music. A system network and methods are provided for designing and developing parameter mapping configurations (SMCs) used in the automated music composition and generation engine so as to enable the automated music composition and generation engine to automatically compose and generate music in response to musical experience descriptors and time and/or space parameters provided as input to the system.

An automated music composition and generation system having a system user interface operably connected to an automated music composition and generation engine, and supporting a method of composing a piece of digital music using musical experience descriptors as to indicate what, when and how particular musical events should occur in the piece of digital music to be automatically composed and generated. The method uses the system user interface to select one or more musical experience descriptors and applying the musical experience descriptors along a timeline representation of a piece of digital music to be automatically composed and generated by the automated music composition and generation engine.

Systems and techniques for removing a sound recording from an audio recording (e.g., an audio recording embedded in a media file) are presented. The system can include an identification component, a first subtraction component and a second subtraction component. The identification component identifies a sound recording in a mixed audio recording. The first subtraction component determines a local linear transformation of the sound recording and subtracts the local linear transformation of the sound recording from the mixed audio recording to generate a new mixed audio recording. The second subtraction component compares one or more segments of the sound recording with one or more corresponding segments of the new mixed audio recording and reduces a power level of the new mixed audio recording based at least in part on correlation of the one or more corresponding segments with the one or more segments.

An automated music composition and generation system having a system user interface operably connected to an automated music composition and generation engine, and supporting a method of scoring a selected media object with one or more pieces of digital music. The method uses the system user interface to select one or more musical experience descriptors and then apply the selected musical experience descriptors to the selected digital media object to indicate what, when and how particular musical events should occur in the one or more pieces of digital music to be automatically composed and generated by the automated music composition and generation engine. The generated piece of digital music is then used in musically scoring the selected digital media object.

A method of automatically confirming the uniqueness of digital pieces of music produced by an automated music composition and generation system while satisfying the creative intentions of system users. The method involves reviewing, selecting and providing one or more musical experience descriptors and 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 music piece analysis subsystem for automatically examining each piece of composed music that has been generated by said automated music composition and generation engine, comparing the digital piece of composed and generated music against other digital pieces of music composed and generated by said automated music composition and generation system for said system user, and determining whether or not the examined digital piece of composed and generated music is sufficiently unique. Also, the method automatically confirms with the system user that each examined digital piece of composed and generated music satisfies the creative intentions of the system user.