Example methods for generating an animated character in dance poses to music may include generating, by at least one processor, a music input signal based on an acoustic signal associated with the music, and receiving, by the at least one processor, a model output signal from an encoding neural network. A current generated pose data is generated using a decoding neural network, the current generated pose data being based on previous generated pose data of a previous generated pose, the music input signal, and the model output signal. An animated character is generated based on a current generated pose data; and the animated character caused to be displayed by a display device.

A music analysis method realized by a computer includes calculating an evaluation index of each of a plurality of structure candidates formed of N analysis points selected in different combinations from K analysis points in an audio signal of a musical piece, and selecting one of the plurality of structure candidates as a boundary of a structure section of the musical piece in accordance with the evaluation index of each of the plurality of structure candidates. N is a natural number greater than or equal to 2 and less than K, and K is a natural number greater than or equal to 2.

Techniques are disclosed relating to automatically generating new music content based on image representations of audio files. A music generation system includes a music generation subsystem and a music classification subsystem. The music generation subsystem may generate output music content according to music parameters that define policy for generating music. The classification subsystem may be used to classify whether music is generated by the music generation subsystem or is professionally produced music content. The music generation subsystem may implement an algorithm that is reinforced by prediction output from the music classification subsystem. Reinforcement may include tuning the music parameters to generate more human-like music content.

An adaptive music playback system is disclosed. The system includes a composition system that receives information corresponding to user activity levels. The composition system modifies the composition of a song in response to changes in user activity. The modifications are made according to a set of composition rules to facilitate smooth musical transitions.

A generation system (100) of synthesized sound comprises: a first stage (1), wherein features (F) are extracted from an input raw sound and parameters of said features are evaluated; a second stage (2) wherein the evaluated parameters are used to create a plurality of physical models that are metrically evaluated in order to find the parameters of the best physical model, and a third stage (3) wherein the parameters of the best physical model are perturbed in order to create perturbed physical models and a metric evaluated of the perturbed physical models is performed to find the parameters of the best physical model.

A media-content augmentation system includes a processing system that receives input data in the form of temporally-varying events data. The processing system resolves the input into one or more categorized contextual themes, correlates the themes with metadata associated with at least one reference media file, and then splices or fades together selected parts of the media file, thus generating as an output, a media product in which transitions between its contextual themes are aligned with selected temporal events in the input data. The temporarily-varying events take the form of a beginning and an end in the case of a sustained feature, or a specific point in time for a hit point. A method aligns sections in digital media files with temporally-varying events data to compose a media product. The system augments a sensory experience of a user by dynamically changing and then playing selected media files within the context of the categorized themes input to the processing system.

A method for processing a voice signal by an electronic system to create a song is disclosed. The method comprises the steps in the electronic system of acquiring an input singing voice recording (11); estimating a musical key (15b) and a Tempo (15a) from the singing voice recording (11); defining a tuning control (16) and a timing control (17) able to align the singing voice recording (11) with the estimated musical key (15b) and Tempo (15a); applying the tuning control (16) and the timing control (17) to the singing voice recording (11) so that an aligned voice recording (20) is obtained. Next, the method comprises the step of generating an music accompaniment (23) as function of the estimated musical key (15b) and Tempo (15a) and an arrangement database (22) and mixing the aligned voice recording (20) and the music accompaniment (23) to obtain the song (12). A system a server and a device are also disclosed.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).

Computer-based systems, devices, and methods for assigning mood labels to musical compositions are described. A mood classifier is trained based on mood-labeled musically-coherent segments of musical compositions and subsequently applied to automatically assign mood labels to musically-coherent segments of musical compositions. In both cases, the musically-coherent segments are generated using automated segmentation algorithms.

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.