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.

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.

New method for improving the reproduction of stereophonic modulations in cars.

According to this method, in first step the level distortion in uncorrelated isobar mode is canceled first by means of the balance adjustment, in a second stage the center of the sound image is positioned at the center of the sound scene in the isobaric correlated mode in delaying the modulation feeding the loudspeaker closest to the conductor and in a third stage by canceling the stereophonic phase distortion in the isobaric mode correlated by means of a phase equalizer.

Conventionally, significant time and effort are required to construct audio tracks. Disclosed embodiments enable automation of audio tracks using templates that associate sound generator(s) with template section(s). Each template enables a model to automatically generate unique audio tracks in which the sections and/or sounds are probabilistically determined. Certain embodiments introduce additional variability into the automated generation of audio tracks. In addition, the model may generate the audio tracks, note by note, to ensure that no copyrights are infringed.

In an embodiment, a method is provided for determining a mood profile of media data. In this method, mood is determined across multiple elements of mood for the media data to create a mood profile associated with the media data. In some embodiments, the mood profile is then used to determine congruencies between one or more pieces of media data.

The present invention relates to a computer implemented system and method for generating an audio output file. The method including using one or more processors to perform steps of: receiving audio tracks, each audio track created according to audio parameters; separating each audio track into at least one selectable audio block, each audio block including audio content from a musical instrument involved in creating the audio track; assigning a unique identifier to each audio block; using the unique identifiers to select audio blocks, and generating the audio output by combining the audio blocks. The present invention prevents the use of the same combination of audio blocks in the generation of audio output to ensure that the audio output files generated a sufficiently unique. Also provided are audio file recording, editing and mixing modules enabling a user to have full creative control over mix and other parameters to modify as desired the audio file generated.

According to a first embodiment, one method presented herein involves methods of finding the best fitting song from a large audio database for a selected video production. The content of these songs utilized in the instant invention has been tagged by emotion tags describing the energy, the emotion of these songs over time—meaning that each song can contain a plurality of, even overlapping, emotions.

According to a first embodiment, there is presented herein a method of increasing the energy level of a user-selected song in a loop-based music generation system. In one embodiment the algorithm is integrated into a music generation/song construction process and comprises of three different approaches, with one of the three being a hybrid version of the remaining approaches.

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.

In some embodiments, an electronic device displays time-synced lyrics of content items playing on an electronic device. In some embodiments, an electronic device displays representations of content items in a playback sequence on an electronic device. In some embodiments, an electronic device shares an item of content with another user account of another electronic device.