A system and method to generate and implement digital tuning files based on p-smooth number sequences for use with a digital musical instrument is provided. A p-smooth number sequence is generated and a subset of p-smooth numbers from the sequence is chosen as a musical octave of musical note frequencies. Each note within the octave is designated as a tonic, and each tonic is used to generate additional musical octaves and corresponding musical note notations. The musical octaves and corresponding musical note notations are stored into the digital memory of the digital musical instrument, and the instrument's native musical note mappings are remapped to the musical octaves and corresponding musical note notations from the digital file.

A method of monitoring an operating event of a combustion engine includes receiving a noise signal sensed by a knock sensor disposed in or proximate to the combustion engine, correlating the noise signal with a musical instrument digital interface (MIDI) fingerprint having at least an ADSR envelope indicative of the operating event, and detecting if the operating event has occurred based on the correlating of the noise signal with the fingerprint.

A chord information extraction device includes a character group extractor, a determiner and a corrector. The character group extractor extracts a character group corresponding to chord information from score image data representing a music score. The determiner determines whether the character group extracted by the character group extractor follows a predetermined chord notation rule. In a case where the character group extracted by the character group extractor does not follow the chord notation rule, the corrector corrects the extracted character group to follow the chord notation rule.

Systems and methods for mapping parts in a digital sheet music file for a harmony. The method may include receiving a selection of a music segment for part mapping, receiving a digital sheet music representation of the selected music segment, and determining a plurality of plausible part mapping for the digital sheet music representation. A part mapping identifies one or more distinct musical parts in the digital sheet music representation, each of said one or more distinct musical parts corresponding to a performer of the harmony. The method may also include analyzing one or more features of the plurality of plausible part mapping to identify a highest probability part mapping based on previously stored information, and outputting the highest probability part mapping.

Methods, systems, and storage media for generating a beatbox transcript are disclosed. Some examples may include: receiving an audio signal having a plurality of beatbox sounds, generating a spectrogram of the audio signal, processing the spectrogram of the audio signal with a neural network model trained on training samples including beatbox sounds, generating, by the neural network model a beatbox sound activation map including a plurality of activation times for a plurality of beatbox sounds, decoding the beatbox sound activation map into a beatbox transcript and providing the beatbox transcript as an output.

Disclosed is a computer-implemented method and system for generating musical notations for a musical score. The method comprises receiving, via a first user interface, a musical note event of the musical score, processing, via a processing arrangement, the received musical note event of the musical score to determine one or more relevant music profile definitions therefor, defining, via the processing arrangement, one or more parameters to be associated with the received musical note event of the musical score based, at least in part, on the determined one or more relevant music profile definitions therefor and generating, via the processing arrangement, at least one notation output for the received musical note event of the musical score based on the defined one or more parameters associated therewith.

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.

A method includes displaying a user interface of a digital audio workstation, which includes a composition region for generating a composition. The composition region includes a representation of a first MIDI file that has already been added to the composition by a user. The method further includes receiving a user input to import, into the composition region, an audio file. In response to the user input to import the audio file, the method includes importing the audio file, which includes, without user intervention, aligning the audio file with a rhythm of the first MIDI file, modifying a rhythm of the audio file based on the rhythm of the first MIDI file, and displaying a representation of the audio file in the composition region.

The disclosure provides a method and an apparatus for song generation. A text input may be received. A topic and an emotion may be extracted from the text input. A melody may be determined according to the topic and the emotion. Lyrics may be generated according to the melody and the text input. A song may be generated at least according to the melody and the lyrics.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.