An information processing system including a selection unit that selects one or more pieces of content in a use status that meets a predetermined condition in a specific period, an extraction unit that extracts, as a feature value of the content, information associated with one or more pieces of material data used at the time of generation of the content, on the basis of metadata added to each piece of the selected content, and a generation unit that generates support information for a user on the basis of the extracted feature value.

Example methods, apparatus and articles of manufacture to determine semantic information for audio are disclosed. Example apparatus disclosed herein are to process an audio signal obtained by a media device to determine values of a plurality of features that are characteristic of the audio signal, compare the values of the plurality of features to a first template having corresponding first ranges of the plurality of features to determine a first score, the first template associated with first semantic information, compare the values of the plurality of features to a second template having corresponding second ranges of the plurality of features to determine a second score, the second template associated with second semantic information, and associate the audio signal with at least one of the first semantic information or the second semantic information based on the first score and the second score.

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.

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.

Example articles of manufacture and apparatus for producing supplemental information for audio signature data are disclosed herein. An example apparatus includes memory including computer readable instructions. The example apparatus also includes a processor to execute the instructions to at least obtain first audio signature data associated with a first time period of media, obtain first semantic signature data associated with the first time period of the media and second semantic signature data associated with a second time period of the media, and when second audio signature data associated with the second time period of the media is unavailable, identify the media based on the first audio signature data associated with the first time period of media when the second semantic signature data associated with the second time period matches the first semantic signature data associated with the first time period of the media.

Example methods, apparatus and articles of manufacture to determine semantic information for audio are disclosed. Example apparatus disclosed herein are to process an audio signal obtained by a media device to determine values of a plurality of features that are characteristic of the audio signal, compare the values of the plurality of features to a first template having corresponding first ranges of the plurality of features to determine a first score, the first template associated with first semantic information, compare the values of the plurality of features to a second template having corresponding second ranges of the plurality of features to determine a second score, the second template associated with second semantic information, and associate the audio signal with at least one of the first semantic information or the second semantic information based on the first score and the second score.

In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

A computerized process, a system, and non-transitory computer-readable medium having computer-executable instructions for mixing audio tracks according to a template. The process may include receiving at least one request for each of a plurality of time blocks of a template; querying a catalog of songs and/or song portions in a database to compile a candidate list of songs and/or song portions that substantially meet the at least one request of a first time block of the plurality of time blocks; choosing a first song portion and a second song portion from the candidate list for the first time block; compiling the first song portion and the second song portion to form at least a portion of the first time block, including blending a temporal length of the first song portion and the second song portion; and generating an audio file with the plurality of time blocks.

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.

Methods, apparatus, systems and articles of manufacture are disclosed methods and apparatus for audio equalization based on variant selection. An example apparatus includes a processor to obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music and organize the training data into a plurality of entries based on the plurality of reference audio signals, a training model executor to execute a neural network model using the training data, and a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.