This disclosure falls into the field of adapting external content to a video stream, and more specifically it is related to analyzing the video stream to define a suitable narrative model, and adapting the external content based on this narrative model.

A computer-implemented method for creating an immersive audio experience. The method includes receiving a selection of an audio track via a user interface, and receiving audio track metadata for the audio track. The method includes querying an audio database based on the track metadata and determining that audio data for the audio track is not stored on the audio database. The method includes analyzing the audio track to determine one or more audio track characteristics. The method includes generating vibe data based on the one or more audio track characteristics, wherein the vibe data includes time-coded metadata. Based on the vibe data, generating visualization instructions for one or A/V devices in communication with a user computing device, and transmitting the generated visualization instructions and the audio track to the user computing device.

An example method involves comparing a primary element of a first piece of audio data to a primary element of a second piece of audio data; based on the comparing of the primary elements, determining that the first and second pieces of audio data have the same predominant mood category; in response to determining that the first and second pieces of audio data have the same predominant mood category, comparing a first mood score of the primary element of the first piece of audio data to a second mood score of the primary element of a second piece of audio data; determining that an output of the comparison of the two mood scores exceeds a threshold value; and in response to determining that the output of the comparison of the two mood scores exceeds the threshold value, providing an indicator to an application.

A system for creating music using visual input. The system detects events and metrics (e.g., objects, gestures, etc.) in user input (e.g., video, audio, music data, touch, motion, etc.) and generates music and visual effects that are synchronized with the detected events and correspond to the detected metrics. To generate the music, the system selects parts from a library of stored music data and assigns each part to the detected events and metrics (e.g., using heuristics to match musical attributes to visual attributes in the user input). To generate the visual effects, the system applies rules (e.g., that map musical attributes to visual attributes) to translate the generated music data to visual effects. Because the visual effects are generated using music data that is generated using the detected events/metrics, both the generated music and the visual effects are synchronized with—and correspond to—the user input.

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.

Example embodiments relate to methods and systems for playback of adaptive music corresponding to an athletic activity. A user input is received from a user selecting an existing song for audible playback to the user, the song comprising a plurality of audio layers including at least a first layer, a second layer, and a third layer. Augmented playback of the existing song to the user is initiated by audibly providing the first layer but not the second layer. Physical activity information derived from a sensor corresponding to a real-time physical activity level of a user is received. If the physical activity level of the user is above a first activity level threshold, the augmented playback of the existing song is continued by audibly providing the first layer and the second layer to the user.

A lyrics analyzer generates tags and explicitness indicators for a set of tracks. These tags may indicate the genre, mood, occasion, or other features of each track. The lyrics analyzer does so by generating an n-dimensional vector relating to a set of topics extracted from the lyrics and then using those vectors to train a classifier to determine whether each tag applies to each track. The lyrics analyzer may also generate playlists for a user based on a single seed song by comparing the lyrics vector or the lyrics and acoustics vectors of the seed song to other songs to select songs that closely match the seed song. Such a playlist generator may also take into account the tags generated for each track.

A method, system and processes to develop a patient tailored music therapy based on Indian Classical Music compositions to treat ASD (Autistic Spectrum Disorders) and ADHD (Attention Deficit Hyperactivity Disorder) is described. According to this present invention there is provided a method to develop a tailored music therapy for treating patients suffering from ASD (Autistic Spectrum Disorders) and ADHD (Attention Deficit Hyperactivity Disorder) based on the patient's response and a system to measure response of the patient to music therapy and mindfulness inputs. This invention comprises of a process to determine suitable Indian Classical Music compositions playlist for use in treating the patient (FIG. 1) followed by further tuning of the selections allowable note levels, ramp up and ramp down times to and from allowable note levels, melody hold times and rhythm pattern selection to develop an optimum waveform (FIG. 2) all based on measuring the patient response using a multiple input—physical movement, audio and brain wave response measurement system (FIG. 3) or thru visual observations. The invention also provides a process to determine daily therapy and mindfulness time and a process for monthly music therapy and mindfulness tailoring. The invention also provides a system (FIG. 3) to measure patient response to the music therapy and mindfulness, which can be used in conjunction with or in place of visual observations. In this invention the patient starts off with a therapy and mindfulness tailoring session where a playlist of Indian Classical Music Raga compositions is first developed, selected based on the patient's response as measured by the system provided in FIG. 3 or thru visual observations. Then patient specific optimum note level, beat rhythm pattern and rhythm pattern frequency, ramp up to and down times from optimum note levels are determined based on the patient's response to create a waveform (FIG. 2). The playlist selections are then modified manually or by a computer program using the waveform parameters and when played to the patient elicits a Calm Range Response pattern defined by a state of stimulated mindfulness but not falling asleep characterized by specific range of motion, audio or brain wave response unique to the patient. The specific pieces of the waveform are derived by varying waveform parameters and measuring the response of the patient (FIGS. 4 A, B, C, D) using the response measuring system (FIG. 3) or thru visual observations. The invention also describes a process to develop daily listening period duration (FIG. 5). The invention describes a process used

In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

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.