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.

A system is described that permits identified musical phrases or themes to be synchronized and linked into changing real-world events. The achieved synchronization includes a seamless musical transitionachieved using a timing offset, such as relative advancement of an significant musical onset, that is inserted to align with a pre-existing but identified music signature, beat or timebasebetween potentially disparate pre-identified musical phrases having different emotive themes defined by their respective time signatures, intensities, keys, musical rhythms and/or musical phrasing. The system operates to augment an overall sensory experience of a user in the real world by dynamically changing, re-ordering or repeating and then playing audio themes within the context of what is occurring in the surrounding physical environment, e.g. during different phases of a cardio workout in a step class the music rate and intensity increase during sprint periods and decrease during recovery periods.

The system generates a recommendation for collaborative content to be consumed, thus allowing a large field of content to be parsed. The content may include audio content, video content, image content, or other content. The system identifies a collaborative content and a base content upon which the collaborative content is generated. Based on analysis of the collaborative content and profile information, the system determines recommendation metric, or a score. Based on the metric, the system generates a recommendation of the collaborative content. Collaborative content that is better formed based on signal properties, favorably compared to the base content, created by more highly rated users, or formatted in a preferred way may be more strongly recommended. Signal properties include temporal, spectral, audio, or visual properties of the content. The system outputs the recommendation for storage, display, or both to provide guidance to users consuming or reviewing collaborative content.

A media-content augmentation system includes a processing system that receives input data in the form of temporally-varying events data. The processing system resolves the input into one or more categorized contextual themes, correlates the themes with metadata associated with at least one reference media file, and then splices or fades together selected parts of the media file, thus generating as an output, a media product in which transitions between its contextual themes are aligned with selected temporal events in the input data. The temporarily-varying events take the form of a beginning and an end in the case of a sustained feature, or a specific point in time for a hit point. A method aligns sections in digital media files with temporally-varying events data to compose a media product. The system augments a sensory experience of a user by dynamically changing and then playing selected media files within the context of the categorized themes input to the processing system.

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 automated music composition and generation system and process for producing one or more pieces of digital music, by providing a set of musical energy (ME) quality control parameters to an automated music composition and generation engine, applying certain of the selected musical energy quality control parameters as markers to specific spots along the timeline of a selected media object or event marker by the system user during a scoring process, and providing the selected set of musical energy quality control parameters to drive the automated music composition and generation engine to automatically compose and generate one or more pieces of digital music with control over the specified qualities of musical energy embodied in and expressed by the piece of digital music to composed and generated by the automated music composition and generation engine.

According to an embodiment, there is provided a system and method for automatically generating a complete music work from a partially completed work provided by a user. One approach uses an artificial intelligence (AI) engine that is trained by creating incomplete works from a database of complete works and then instructing the AI to complete the incomplete works. A comparison is made between the completed works and the originals to determine the effectiveness of the training process. After the AI is trained, it is applied to the user's incomplete work to produce a final music item.

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.

Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments. Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labelling, characterizing, or otherwise processing music.

Due to discrepancies in musical timing signatures, the invention assesses whether a recorded displacement, expressed in terms of beats and fractions, between exit and entry points for a potential musical splice or cut, corresponds to permit a seamless music splicing of different musical sections. Assessment is achieved by establishing a third time base of pulses having a length dependent upon a lowest common multiple of fractions within respective bars for different sections, with the bars of the respective sections then partitioned into an equal number of fixed length pulses. A coefficient aligns different time signatures; it is a ratio between pulses within the different sections. The coefficient identifies corresponding locations of a cut point, related to a suitable anacrusis, in terms of respectively an aligned bar, beat, quaver and fraction in differing time signatures. The coefficient ensures that the time anacrusis in one time signature is interchangeable with others.