Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: An operator of a digital audio workstation (DAW) application is able to assign individual tracks of a DAW session for export to specific players within a musical score of a scorewriter application. The DAW operator associates each track with a player identifier, which is retained in association with an interoperable format file generated by the export process. When the scorewriter imports such a file, it extracts the player identifier and uses it to map the track to a scored instrument. The mapping may also depend on a scorewriter arrangement of players for the instruments. The DAW operator may assign multiple tracks representing a given instrument played with different techniques to a single instrument part in a score. The playing techniques for the instruments are also associated with the tracks and may be parsed by the scorewriter to annotate the score with the corresponding notations.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: An operator of a digital audio workstation (DAW) application is able to assign individual tracks of a DAW session for export to specific players within a musical score of a scorewriter application. The DAW operator associates each track with a player identifier, which is retained in association with an interoperable format file generated by the export process. When the scorewriter imports such a file, it extracts the player identifier and uses it to map the track to a scored instrument. The mapping may also depend on a scorewriter arrangement of players for the instruments. The DAW operator may assign multiple tracks representing a given instrument played with different techniques to a single instrument part in a score. The playing techniques for the instruments are also associated with the tracks and may be parsed by the scorewriter to annotate the score with the corresponding notations.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.