Methods, devices, systems, and computer programs are presented for providing music while exercising. One method includes an operation for receiving a request for a music segment from a computing device. The request includes the pace of exercise of a user associated with the computing device. The music segment is selected based on the pace, and the music segment is modified to correlate the tempo of the music segment to the pace of exercise. The modified music segment is sent to the computing device to be played in one or more speakers to provide music that is correlated to the exercise.

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.

A trained model establishment method realized by a computer includes acquiring a plurality of datasets each of which is formed by a combination of first performance data of a first performance by a performer, second performance data of a second performance performed together with the first performance, and a satisfaction label indicating a degree of satisfaction of the performer, and executing machine learning of a satisfaction estimation model by using the plurality of datasets. In the machine learning, the satisfaction estimation model is trained such that, for each of the datasets, a result of estimating a degree of satisfaction the performer from the first performance data and the second performance data matches the degree of the satisfaction indicated by the satisfaction label.

A mobile music listening device synchronizing in a personalized way music and movement, and dedicated to improving the kinematics of the runner. Thanks to inertial units connected to a smartphone, the runner's steps are detected in real time by the mobile application. A dedicated algorithm adapts the pulsation of the musical excerpts in such a way as to bring the runner to a suitable cadence, capable of preventing injuries. A method for the synchronization of the rhythmic stimulation with the biological variability using a Kuramoto model characterized in that phase oscillator with a coupling term from the movement dynamics with parameters of, coupling strength, maximum and minimum frequencies for a fraction of the unmodified song frequency, maximum difference between the tempo and target frequency, Target the target frequency.

Provided is an information processing device including an acquisition unit that acquires subjective evaluation information from a second user about each performance performed by at least part of a body of a first user moving, a learning unit that performs machine learning on a relationship between the each performance and the corresponding subjective evaluation information and generates relationship information between the each performance and the corresponding subjective evaluation information, and a presentation unit that presents feedback information to the second user based on the relationship information.

A method and a system for synthesizing beat sound and music. The method comprises: selecting a sound material as a beat sound (S101); intelligently or manually regulating, according to a human body characteristic parameter, a beat sound frequency matched with the human body characteristic parameter (S102); selecting music (S103); and synthesizing the beat sound and the music in a volume ratio to obtain mixed music (S104). The method and system for synthesizing beat sound and music provided by the present disclosure can intelligently or manually regulate the beat sound frequency and the volume ratio of the beat sound and the music through human body characteristic parameter, and obtain the mixed music in various synthesis ways. The mixed music can be used in trainings for physical and mental relaxations and improving the concentration of the human body, and is suitable for practices of improving sleeping conditions of the human body, and improving practice effects of yoga, meditation, and zazen.

Systems and methods are described in which the location of a tap on the body of a handheld device is determined in real time using data streams from an embedded inertial measurement unit (IMU). Taps may be generated by striking the handheld device with an object (e.g., a finger), or by moving the handheld device in a manner that causes it to strike another object. IMU accelerometer, gyroscopic and/or orientation (relative to the magnetic and/or gravitational pull of the earth) measurements are examined for signatures that distinguish a tap at a location on the body of the device compared with signal characteristics produced by taps at other locations. Neural network and/or numerical methods may be used to perform such classifications. Tap locations, tap timing and tap attributes such as the magnitude of applied forces, device orientation, and the amplitude and directions of motions during and following a tap, may be used to control or modulate responses within the handheld device and/or actions within connected devices.

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 massage device according to one embodiment of the present disclosure. The massage device comprises: a body structure for forming an area to accommodate at least one portion of the body of a user and defining the outer shape of the massage device; a massage module for supplying a massage function to the at least one portion of the body of the user accommodated in the area in the body structure; an input unit for receiving an input of an arbitrary form from the user of the massage device; a display for displaying massage mode screen information which can be selected by the user of the massage device; and a control unit for controlling one more operations of the massage device on the basis of at least a part of the input of an arbitrary form received by the input unit. The massage mode screen information may include: an automatic mode graphic object linked to a function of performing an automatic mode consisting of a predetermined massage pattern and massage time; a manual mode graphic object linked to a function of performing a manual mode to allow a user of the massage device to configure a massage pattern and massage time; and a massage option mode graphic object linked to a function enabling selection of a massage option mode consisting of an additional function that provides convenience to a user of the massage device in addition to a massage operation that applies pressure to at least a part of the body of the user of the massage device.

A method, system, and medium for affective music recommendation and composition. A listener's current affective state and target affective state are identified, and an audio stream, such as a music playlist, is generated with the intent of effecting a controlled trajectory of the listener's affective state from the current state to the target state. The audio stream is generated by a machine learning system trained using data from the listener and/or other users indicating the effectiveness of specific audio segments, or audio segments having specific features, in effecting the desired affective trajectory. The audio stream is presented to the user as an auditory stimulus. The machine learning system may be updated based on the affective state changes induced in the listener after exposure to the auditory stimulus. Over time, the machine learning system gains a robust understanding of the relationship between music and human affect, and thus the machine learning system may also be used to compose, master, and/or adapt music configured to induce specific affective responses in listeners.