A musical piece inference device includes an electronic controller configured to execute a data acquisition module, an inference module, and an output module. The data acquisition module is configured to acquire target data including an input token sequence that is arranged to indicate at least a part of a musical piece and includes a plurality of bar-line/beat tokens arranged to indicate bar-line/beat positions of at least the part of the musical piece. The bar-line/beat positions are positions of bar lines of at least the part of the musical piece, positions of beats of at least the part of the musical piece, or both. The inference module is configured to, by using a trained inference model, generate an output token sequence indicating a result of an inference with respect to the musical piece from the input token sequence. The output module is configured to output the result of the inference.

Embodiments are disclosed for generating an audio segmentation of an audio sequence using deep embeddings. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and extracting features for each frame of the audio sequence, where each frame is associated with a beat of the audio sequence. The method may further comprise clustering frames of the audio sequence into one or more clusters based on the extracted features and generating segments of the audio sequence based on the clustered frames, where each segment includes frames of the audio sequence from a same cluster. The method may further comprise constructing a multi-level audio segmentation of the audio sequence and performing a segment fusioning process that merges shorter segments with neighboring segments based on cluster assignments.

An information processing device (10) includes a decision unit (23e) and a generation. unit (23f). The decision unit (23e) decides connection order of divided scenes obtained by dividing a free-viewpoint video based on a multi-viewpoint video obtained by imaging content, based on a feature amount of a given. sound and similarities between respective connection frames in the divided scenes. The generation unit (23f) generates free-viewpoint content in which the divided scenes are connected in the connection order decided by the decision unit (23e).

A system, method and computer product for combining audio tracks. In one example embodiment herein, the method comprises determining at least one music track that is musically compatible with a base music track, aligning those tracks in time, and combining the tracks. In one example embodiment herein, the tracks may be music tracks of different songs, the base music track can be an instrumental accompaniment track, and the at least one music track can be a vocal track. Also in one example embodiment herein, the determining is based on musical characteristics associated with at least one of the tracks, such as an acoustic feature vector distance between tracks, a likelihood of at least one track including a vocal component, a tempo, or musical key. Also, determining of musical compatibility can include determining at least one of a vertical musical compatibility or a horizontal musical compatibility among tracks.

Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments.

Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labeling, characterizing, or otherwise processing music.

An information processing method is an information processing method including generating an output track by using an input track including a plurality of first information elements provided over a certain period or a certain section and a learned model (21), in which the output track includes a first track that is a same track as the input track or a changed track, and a second track including a plurality of second information elements provided over the certain period or the certain section, and the learned model (21) is a learned model generated by using training data so as to output output data corresponding to the output track when input data corresponding to the first track is input.

An apparatus, method, and computer-readable storage medium that generate at least a cue point in a musical piece. The method includes generating a beat grid representing the musical piece, determining values for the beat grid, the values corresponding to an audio feature of the musical piece, and each value representing an entire duration of each beat in the beat grid of the musical piece, calculating a score for the audio feature at each of a plurality of positions in the beat grid of the musical piece, using some or all of the determined values, and generating the cue point at a particular position of the plurality of positions, based on the calculated scores.

Technologies are described for identifying familiar or interesting parts of music content by analyzing changes in vocal power using frequency spectrums. For example, a frequency spectrum can be generated from digitized audio. Using the frequency spectrum, the harmonic content and percussive content can be separated. The vocal content can then be separated from the harmonic and/or percussive content. The vocal content can then be processed to identify surge points in the digitized audio. In some implementations, the vocal content is included in the harmonic content during the separation procedure and is then separated from the harmonic content.

A musical piece structure analysis method includes acquiring an acoustic signal of a musical piece, extracting a first feature amount indicating changes in tone from the acoustic signal of the musical piece, extracting a second feature amount indicating changes in chords from the acoustic signal of the musical piece, outputting a first boundary likelihood indicating likelihood of a constituent boundary of the musical piece from the first feature amount using a first learning model, outputting a second boundary likelihood indicating likelihood of the constituent boundary of the musical piece from the second feature amount using a second learning model, identifying the constituent boundary of the musical piece by performing weighted synthesis of the first boundary likelihood and the second boundary likelihood, and dividing the acoustic signal of the musical piece into a plurality of sections at the constituent boundary that has been identified.

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.