An approach is provided in which an information handling system configures a reinforcement learning model based inspiration selections received from a user. The information handling system performs training iterations using the configured reinforcement learning model, which generates multiple actions and multiple rewards corresponding to multiple actions. The information handling system determines that the multiple rewards reach an empirical threshold and, in turn, generates a musical composition based on the multiple actions.

A method is provided including separating a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each instrument track of each of the first plurality and second plurality corresponds to a type of instrument; selecting a first instrument track from the first plurality of instrument tracks and a second instrument track from the second plurality of instrument tracks based at least on the type of instrument corresponding to the first instrument track and the second instrument track; fading out other instrument tracks from the first plurality of instrument tracks; performing a crossfade between the first instrument track and the second instrument track; and fading in other instrument tracks from the second plurality of instrument tracks.

A method for training one or more AI models for generating audio scores accompanying visual datasets includes obtaining training data comprising a plurality of audiovisual datasets and analyzing each of the plurality of audiovisual datasets to extract multiple visual features, textual features, and audio features. The method also includes correlating the multiple visual features and textual features with the multiple audio features via a machine learning network. Based on the correlations between the visual features, textual features, and audio features, one or more AI models are trained for composing one or more audio scores for accompanying a given dataset.

An accompaniment classification method and apparatus is provided. The method includes the following. A first type of audio features of a target accompaniment is obtained (S301, S401). Data normalization is performed on each kind of audio features in the first type of audio features of the target accompaniment to obtain a first feature-set of the target accompaniment and the first feature-set is input into a first classification model for processing (S302, S402). A first probability value output by the first classification model for the first feature-set is obtained (S303, S403). An accompaniment category of the target accompaniment is determined to be a first category of accompaniments when the first probability value is greater than a first classification threshold (S404). The accompaniment category of the target accompaniment is determined to be other categories of accompaniments when the first probability value is less than or equal to the first classification threshold.

A computer implemented method for automatically generating musical works including receiving a lyrical input and receiving a musical input. The method includes analyzing the lyrical input to determine at least one lyrical characteristic and analyzing the musical input to determine at least one musical characteristic. Based on the at least one lyrical characteristic, the method includes correlating the lyrical input with the musical input to generate a synthesizer input. The method includes sending the synthesizer input and the at least one voice characteristic to a voice synthesizer. The method may also include receiving, from the voice synthesizer, a vocal rendering of the lyrical input. The method includes receiving a singer selection corresponding to at least one voice characteristic, and generating a musical work from the vocal rendering based on the lyrical input, the musical input, and the at least one voice characteristic.

Equalizer controller and controlling method are disclosed. In one embodiment, an equalizer controller includes an audio classifier for identifying the audio type of an audio signal in real time; and an adjusting unit for adjusting an equalizer in a continuous manner based on the confidence value of the audio type as identified.

A method of making audio music selection and creating a mixtape, comprising importing song files from a song repository; sorting and filtering the song files based on selection criteria; and creating the mixtape from the song files sorting and filtering results. The sorting and filtering of the song files comprise: spectral analyzing each of the song files to extract low level acoustic feature parameters of the song file; from the low level acoustic feature parameter values, determining the high level acoustic feature parameters of the analyzed song file; determining a similarity score of each of the analyzed song files by comparing the acoustic feature parameter values of the analyzed song file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed song files according to their similarity scores; and filtering out the analyzed song files with first similarity scores lower than a filter threshold.

Embodiments of the present disclosure relate to a sensory experience providing apparatus for providing a sensory experience based on sound in a vehicle, and a method thereof. The controller is configured to receive a sound played in the vehicle, extract a sound feature from the received sound, generate sensory information based on the extracted sound feature, and provide a sensory experience based on the sensory information.

A method for processing an input sound signal of singing voice, to obtain a sound signal with an impression different from the input sound signal, includes: selecting a genre from among a plurality of tune genres in accordance with a selection operation by a user, setting, to a first unit, a set of first parameters corresponding to the selected genre, displaying a first impression identifier corresponding to the selected genre for a first control of a first user parameter in the set of first parameters, changing the first user parameter in accordance with a change operation on the first control by the user, and strengthening, by the first unit, signal components within a particular frequency band of the sound signal, in accordance with the set of first parameters including the first user parameters.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using complex linear projection are disclosed. In one aspect, a method includes the actions of receiving audio data corresponding to an utterance. The method further includes generating frequency domain data using the audio data. The method further includes processing the frequency domain data using complex linear projection. The method further includes providing the processed frequency domain data to a neural network trained as an acoustic model. The method further includes generating a transcription for the utterance that is determined based at least on output that the neural network provides in response to receiving the processed frequency domain data.