Provided is an electrophonic chordophone system (20) comprising sensor (24) configured to be operatively responsive to respective strings of a guitar (22). The system (20) also includes non-transitory processor-readable storage means (26) which contains first and second user-configurable tonal formats (28) and (30). Also included is processor (32), arranged in signal communication with the sensor (24) and storage means (26), and adapted to associate a melody group of notes producible by the strings with the first tonal format (28) in a one-to-one mapping or direct correlation, and to associate a control group of notes producible by the strings with the second tonal format (30) in a one-to-many mapping or indirect correlation. Also included is a synthesiser (34), arranged in signal communication with the processor (32), and configured to produce both the first and second tonal formats simultaneously in substantial real-time. The first tonal format (28) is actuatable via the melody group of notes and the second tonal format (30) is dynamically selectable via the control group of notes and also actuatable via the melody group of notes. In this manner, a melody is producible via the first tonal format (28) with a dynamic backing track producible via the second tonal format (30), all using one guitar (22).

The disclosed technology relates to a process for detecting musical artifacts within a musical composition. The detection of musical artifacts is based on analyzing the energy and frequency of the digital signal of the musical composition. The identification of musical artifacts within a musical composition would be used in connection with audio-video editing.

In transposing a piece of music in accordance with the sound range of a musical instrument or the vocal range of a singer, an amount of transposition is determined in consideration of the sound range and a key signature resulting from the transposition. A transposing device according to an embodiment of the present invention includes a data acquisition unit configured to acquire a predetermined sound range, key information of a predetermined piece of music, and pitch information of sounds constituting the predetermined piece of music and a transposition amount determination unit configured to calculate a value of transposition evaluation on the basis of the sound range, the key information of the piece of music, and the pitch information and to determine an amount of transposition on the basis of the value of transposition evaluation.

An electronic device comprising circuitry configured to perform (402; 702; 1204) source separation (201) based on a received audio input to obtain a separated source, to perform onset detection (202) on the separated source to obtain an onset detection signal and to mix (405; 706; 1207) the audio signal with the separated source based on the onset detection signal to obtain an enhanced separated source.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal; obtaining a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

Described herein are musical translation devices and methods of use thereof. Exemplary uses of musical translation devices include optimizing the understanding and/or recall of an input message for a user and improving a cognitive process in a user.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

Computer-based systems, devices, and methods for generating variations of musical compositions are described. Musical compositions stored in digital media include one or more music data object(s) that encode notes. A first set of notes is characterized and a transformation is applied to replace at least one note in the first set of notes with at least one note in a second set of notes. The transformation may explore or call upon the full range of musical notes available without being constrained by conventions of musicality and harmony. For each particular note in the second set of notes that replaces a note in the first set of notes, whether the particular note is in musical harmony with other notes in the music data object is separately assessed and, if not, the particular note is adjusted to bring it into musical harmony with other notes in the music data object.

Computer-based systems, devices, and methods for generating musical note sequences are described. One or more musical composition(s) stored in digital media include one or more data object(s) that encode notes and/or note sequences. At least one note sequence is processed to form a time-ordered sequence of parallel notes, which is analyzed to determine a k-back probability transition matrix for the at least one note sequence. An attribute, such as a style, of the at least one note sequence is thus encoded and used to generate new note sequences that embody a similar attribute or style. In some implementations, the at least one note sequence may include a concatenated set of note sequences representative of a particular library of musical compositions.