Staffpad Limited

David William Hearn

Matthew Tesch

Disclosed is a method for performing a musical score, the method comprising generating a plurality of score maps using at least one of an electronic representation of the musical score and event-based notations for at least one musical note in the musical score, wherein each score map corresponds to a single performance characteristic of the musical score and contains a plurality of events related to the single performance characteristic, and wherein each of the plurality of score maps is processed by a processing block to generate a plurality of playback characteristic maps.

Method and system for performing musical score

Disclosed is a method for simulating a musical phrase, wherein the musical phrase includes a sequence of notes, the method comprising generating timbral fingerprints associated with a sample library that comprises recordings of a plurality of notes in a plurality of intensities, using a linear predictive coding technique, wherein the timbral fingerprints relate to the plurality of intensities of the plurality of notes; determining an origin intensity for the musical phrase, wherein the origin intensity is one intensity selected from amongst the plurality of intensities; and simulating each note in the sequence of notes, by morphing a recording of each note in the origin intensity according to timbral fingerprints of the note in the plurality of intensities, for simulating the musical phrase.

Method and system for simulating musical phrase

A system for generation of a musical notation from an audio signal, the system comprising at least one processor configured to: obtain the audio signal from an audio source or a data repository; process the audio signal using first machine learning (ML) model(s) to generate a recognition result, wherein the recognition result is indicative of a pitch and a duration of a plurality of notes in the audio signal and their corresponding confidence scores; generate a preliminary musical notation using the recognition result; process the preliminary musical notation using second ML model(s) to determine whether the preliminary musical notation includes one or more errors; and when it is determined that the preliminary musical notation includes one or more errors, modify the preliminary musical notation to generate the musical notation that is error-free or has lesser errors as compared to the preliminary musical notation.

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London, United Kingdom

David William Hearn