Disclosed are a method for controlling a sound quality based on a voice command and an apparatus therefor. According to an exemplary embodiment of the present disclosure, a sound quality control method based on a voice command includes a voice command acquiring step of acquiring a voice command for playing media contents, a voice command analyzing step of recognizing the media contents by analyzing the voice command and generating recognition result information for the media contents, a category determining step of determining a category for the media contents based on the recognition result information, and a sound quality setting control step of determining a play sound quality mode of the media contents based on a determination result of the category.

An electronic device may comprise audio processing circuitry, pace tracking circuitry, and positioning circuitry. The pace tracking circuitry may be operable to selects a tempo of songs for playback, by the audio processing circuitry based on position data generated by the positioning circuitry, a desired tempo, and whether the songs are stored locally or network-accessible. The position data may indicate the pace of a runner during a preceding, determined time interval. The pace tracking circuitry may control the song selection and/or time stretching based on a runner profile data stored in memory of the music device. The profile data may include runner's distance-per-stride data. The electronic device may include sensors operable to function as a pedometer. The pace tracking circuitry may update the distance-per-stride data based on the position data and based on data output by the one or more sensors.

Presentation of a recommendation to a user for individual processing of audio tracks in a digital audio workstation. Training audio tracks are provided to a human sound mixer and responsive to the training audio tracks individually processed training audio tracks are received from the human sound mixer. The training audio tracks and the individually processed training audio tracks are input to a machine to train the machine. Audio processing operations are output from the trained machine and stored in a record of a database.

A method is performed at a server system of a media-providing service. The server system has one or more processors and memory storing instructions for execution by the one or more processors. The method includes receiving a text sentence including a plurality of words from a device of a first user and extracting a plurality of audio snippets from one or more audio tracks. A respective audio snippet in the plurality of audio snippets corresponds to one or more words in the plurality of words of the text sentence. The method also includes assembling the plurality of audio snippets in a first order to produce an audible version of the text sentence. The method further includes providing, for playback at the device of the first user, the audible version of the text sentence including the plurality of audio snippets in the first order.

An artificial intelligence (AI) method includes generating a first musical interaction behavioral model. The first musical interaction behavioral model causes an interactive electronic device to perform a first set of musical operations and a first set of motional operations. The AI method further includes receiving user inputs received in response to the performance of the first set of musical operations and the first set of motional operations and determining a user learning progression level based on the user inputs. In response to determining that the user learning progression level is above a threshold, the AI method includes generating a second musical interaction behavioral model. The second musical interaction behavioral model causes the interactive electronic device to perform a second set of musical operations and a second set of motional operations. The AI method further includes performing the second set of musical operations and the second set of motional operations.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

An automated music composition and generation system including a system user interface for enabling system users to review and select one or more musical experience descriptors, as well as time and/or space parameters; and an automated music composition and generation engine, operably connected to the system user interface, for receiving, storing and processing musical experience descriptors and time and/or space parameters selected by the system user, so as to automatically compose and generate one or more digital pieces of music in response to the musical experience descriptors and time and/or space parameters selected by the system user. Each digital piece of composed and generated music contains a set of musical notes arranged and performed in the digital piece of music. The engine includes: a digital piece creation subsystem and a digital audio sample producing subsystem supported by virtual musical instrument libraries.

Techniques are disclosed relating to generating music content. In one embodiment, a method includes determining one or more musical attributes based on external data and generating music content based on the one or more musical attributes. Generating the music content may include selecting from stored sound loops or tracks and/or generating new tracks based on the musical attributes. Selected or generated sound loops or tracks may be layered to generate the music content. Musical attributes may be determined in some embodiments based on user input (e.g., indicating a desired energy level), environment information, and/or user behavior information. Artists may upload tracks, in some embodiments, and be compensated based on usage of their tracks in generating music content. In some embodiments, a method includes generating sound and/or light control information based on the musical attributes.

Audio translation system includes a feature extractor and a style transfer machine learning model. The feature extractor generates for each of a plurality of source voice files one or more source voice parameters encoded as a collection of source feature vectors, and generates for each of a plurality of target voice files one or more target voice parameters encoded as a collection of target feature vectors. The style transfer machine learning model trained on the collection of source feature vectors for the plurality of source voice files and the collection of target feature vectors for the plurality of target voice files to generate a style transformed feature vector.

An automated music composition and generation process within an automated music composition and generation system driven by lyrical musical experience descriptors. The process involves the system user accessing said automated music composition and generation system, employing an automated music composition and generation engine having a system user interface. The system user interface is used to select and provide musical experience descriptors, including lyrics, to the automated music composition and generation engine for processing by said automated music composition and generation engine. The system user initiates the automated music composition and generation engine to compose and generate music based on the musical experience descriptors and lyrics provided.