Music compilation methods disclosed herein include providing a database. Data is stored therein associating a user with access credentials for a plurality of music streaming services. A first server is communicatively coupled with the database and with multiple third party servers each of which includes a music library associated with the user. A list is stored in the database listing audio tracks of the libraries. A play selector is displayed on a user interface of a computing device communicatively coupled with the first server. User selection of the play selector initiates playback of a sample set, the sample set including portions of audio tracks in the list. The sample set is determined based on contextual information gathered by the computing device, the contextual information not including any user selection. Music compilation systems disclosed herein include systems configured to carry out the music compilation methods.

Implementations of the subject matter described herein provide a solution that enables a machine to automatically generate a melody. In this solution, user emotion and/or environment information is used to select a first melody feature parameter from a plurality of melody feature parameters, wherein each of the plurality of melody feature parameters corresponds to a music style of one of a plurality of reference melodies. The first melody feature parameter is further used to generate a first melody that conforms to the music style and is different from the reference melody. Thus, a melody that matches user emotions and/or environmental information may be automatically created.

Audio data is generated for a vehicle audio system using a portable computing device. Vehicle parameter data is received at the portable computing device from a vehicle. Sound parameter data is generated from the vehicle parameter data. Audio data is generated using a synthesizer based on the sound parameter data. The generated audio data is transmitted to the vehicle audio system from the portable computing device.

A method for automatically generating an audio signal, the method comprising receiving a source audio signal analyzing the source audio signal to identify a musical parameter characteristic thereof obtaining a supplemental audio signal based on the identified musical parameter characteristic and combining the source audio signal and the supplemental audio signal to form an extended audio signal.

A driving sound library provides driving sounds classified in various themes to a user by analyzing frequency characteristics and temporal characteristics of sound sources classified into categories and determining a chord corresponding to each sound source based on the frequency characteristics and the time characteristics. Modulated sound sources in which each sound source is modulated are generated by applying a chord corresponding to each sound source. The driving sound sources are generated using the sound sources and the modulated sound sources as input data. Pitches of the driving sound sources are then changed based on the driving sound sources and engine sound orders corresponding to an engine RPM of a preset range. Scores for each theme for the driving sounds are generated.

A music generation device includes: an acquisition unit that acquires first stream data and second stream data different from the first stream data; an accompaniment generation unit that generates accompaniment information, which is music data indicating an accompaniment, based on a change in the first stream data; a melody generation unit that generates melody information, which is music data indicating a melody, based on a change in the second stream data; a melody adjustment unit that adjusts the melody information in accordance with a key of the accompaniment indicated by the generated accompaniment information; a music combining unit that combines the accompaniment information and the adjusted melody information to generate musical piece information; and an output unit that outputs the generated musical piece information.

A mobility sound generation apparatus of generating a sound suitable for a landscape while driving of a mobility may include an information acquisition device provided in a mobility to acquire information on an outside landscape of the mobility while driving of the mobility; a sound generation device configured to generate a sound corresponding to the information on the outside landscape; and a sound output device configured to output the generated sound, whereby it is possible to acquire information on an outside landscape while driving of the mobility and generate a sound corresponding to the information on the outside landscape and it is possible for facilitating an occupant to admire a landscape by expressing the outside landscape with sound while driving to generate and provide the sound corresponding to water, mountains, buildings or the like forming the outside landscape of the mobility.

The present application discloses and describes neurostimulation systems and methods that include, among other features, (i) neural stimulation through audio with dynamic modulation characteristics, (ii) audio content serving and creation based on modulation characteristics, (iii) extending audio tracks while avoiding audio discontinuities, and (iv) non-auditory neurostimulation and methods, including non-auditory neurostimulation for anesthesia recovery.

Disclosed is a system and method that allows users to customize audible and visible signals, such as music or video, to maintain a pre-determined or specified pace or to achieve a new pace in repetitive motion activities such as, but not limited to, running, walking, swimming, cycling, aerobics, and the like. Other applications of the system and method include, but are not limited to, enhancing the results of medical rehabilitation programs, physical therapy, weight loss programs, disc jockey services, and industries or manufacturing settings where repetitive motion is common and where audible cues designed to help users maintain a consistent pace are useful.

Vehicle machine learning methods include providing one or more computer processors communicatively coupled with a vehicle. Using data gathered from biometric sensors and/or vehicle sensors, a machine learning model is trained to determine a mental state of a driver and/or a driving state corresponding with a portion of a trip. In implementations the mental or driving state may be determined without a machine learning model. Based at least in part on the determined mental state and the determined driving state, one or more interventions are automatically initiated to alter the mental state of the driver. The interventions may include preparing (or modifying) and initiating a music playlist, altering a lighting condition within the vehicle, altering an audio condition within the vehicle, altering a temperature condition within the vehicle, and initiating, altering, or withholding conversation from a conversational agent. Vehicle machine learning systems perform the vehicle machine learning methods.