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.

A method of making audio music selection and creating a mixtape, comprising importing song files from a song repository; sorting and filtering the song files based on selection criteria; and creating the mixtape from the song files sorting and filtering results. The sorting and filtering of the song files comprise: spectral analyzing each of the song files to extract low level acoustic feature parameters of the song file; from the low level acoustic feature parameter values, determining the high level acoustic feature parameters of the analyzed song file; determining a similarity score of each of the analyzed song files by comparing the acoustic feature parameter values of the analyzed song file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed song files according to their similarity scores; and filtering out the analyzed song files with first similarity scores lower than a filter threshold.

A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

A method for processing music audio data, including providing input audio data representing a first piece of music comprising a mixture of musical timbres. The method also includes decomposing the input audio data to generate at least first-timbre decomposed data representing a first timbre selected from the musical timbres of the first piece of music, and second-timbre decomposed data representing a second timbre selected from the musical timbres of the first piece of music. The method also includes applying a transport control to obtain transport controlled first-timbre decomposed data. The method also includes recombining audio data obtained from the transport controlled first-timbre decomposed data with audio data obtained from the second-timbre decomposed data to obtain recombined audio data.

Indicating what should be played in a piece of music with a music document, including: displaying a part of the music document when a user plays the piece; receiving a real-time audio signal of the playing; automatically determining a playing position within the piece of music based on the real-time audio signal; automatically scrolling the music document on a display depending on the playing position; estimating at least the following from the real-time audio signal: activity; tonality; and tempo used in automatically determining the playing position determined from playing speed of the user. The estimating of the activity includes detecting whether the user is producing any sounding notes. The estimating of the tonality is based on an array of chord models that represent different chords that appear in the music document and allow calculating the probability that the corresponding chord is being played in various real-time audio signal segments.

A device including: first and second input units providing first and second input signals of first and second audio tracks, a decomposition unit to decompose the first input audio signal to obtain decomposed signals, a playback unit to start playback of a first output signal obtained from recombining at least first and second decomposed signals at first and second volume levels, respectively, and a transition unit for performing a transition between playback of the first output signal and playback of a second output signal obtained from the second input signal. The transition unit is adapted for reducing the first/second volume levels according to first/second transition functions. The device includes an analyzing unit to analyze an audio signal to determine a song part junction between two song parts. The transition time interval of at least one of the transition functions is set such as to include the song part junction.

Methods and systems are described that are utilized for remotely controlling a musical instrument. A first digital record comprising musical instrument digital commands from a first electronic instrument for a first item of music is accessed. The first digital record is transmitted over a network using a network interface to a remote, second electronic instrument for playback to a first user. Optionally, video data is streamed to a display device of a user while the first digital record is played back by the second electronic instrument. A key change command is transmitted over the network using the network interface to the second electronic instrument to cause the second electronic instrument to playback the first digital record for the first item of music in accordance with the key change command. The key change command may be transmitted during the streaming of the video data.

Computer-based systems, devices, and methods for generating musical compositions that are purposefully synchronized with video are described. A video timeline is defined with various time-markers that demarcate specific events in the video. A music timeline is generated based on the video timeline. The music timeline preserves the various time-markers from the video timeline. A computer-based musical composition system generates a musical composition based on the music timeline. The musical composition includes various musical events that align, synchronize, or coincide with the time-markers such that when the video and musical composition are played together the musical events align, synchronize, or coincide with the demarcated events in the video.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.

The present invention provides a method for processing music audio data, comprising the steps of providing input audio data representing a first piece of music containing a mixture of predetermined musical timbres, decomposing the input audio data to generate at least a first audio track representing a first musical timbre selected from the predetermined musical timbres, and a second audio track representing a second musical timbre selected from the predetermined musical timbres, applying a predetermined first audio effect to the first audio track, applying no audio effect or a predetermined second audio effect, which is different from the first audio effect, to the second audio track, and obtaining recombined audio data by recombining the first audio track with the second audio track.