Abstract: An electronic device receives a first audio content item that includes a plurality of sound sources. The electronic device generates a representation of the first audio content item. The electronic device determines, from the representation of the first audio content item: a representation of an isolated sound source, and frequency data associated with the isolated sound source. Determining the representation of the isolated sound source and the frequency data associated with the isolated sound source includes using a neural network to jointly determine the representation of the isolated sound source and the frequency data associated with the isolated sound source. The electronic device determines that a portion of a second audio content item matches the first audio content item using the representation of the isolated sound source and/or the frequency data associated with the isolated sound source.

Abstract: In some embodiments, a method comprises receiving a first signal indicative of a current emotional state of a user, receiving a second signal corresponding to a desired emotional state of the user, and based on the first and second signals, generating a playlist of media content including a first item and an nth item. The first signal can be received from a wearable sensor. Generating the playlist can comprise selecting items of media content, and arranging the media content in a sequential order such that the playlist transitions from the first item toward the nth item. The method can further comprise playing back, via a playback device, at least the first item of the media content, and while playing back the first item, receiving a third signal indicative of an updated emotional state of the user.

Abstract: An apparatus includes a distribution generator configured to determine a combination of each of first regions in a first image and a corresponding one of second regions in a second image based on feature amounts of the first regions and feature amounts of the second regions, and generate first distribution information indicating distribution of the difference between a first position of a first region in the first image and a second position of a second region in the second image; a similarity generator configured to generate second distribution information indicating distribution of the difference between the first position and the second position, calculate a first similarity between the first image and the second image, and calculate a second similarity between the first image and the second image; and a processor configured to generate a comparison result by comparing the first image and the second image.

Abstract: Described herein are methods and system for analyzing music audio. An example method includes obtaining a music audio track, calculating acoustic features of the music audio track, calculating geometric features of the music audio track in view of the acoustic features, and determining a mood of the music audio track in view of the geometric features.

Abstract: Reconstruction method and devices for two-dimensional signals that are not bandlimited but have a parametric representation with a finite number of degrees of freedom. The signal is reconstructed from the samples obtained after a suitable filtering with a smoothing kernel. Projection techniques allow reducing the problem to a combination of one-dimensional subproblems.

Abstract: An apparatus and method for identifying clusters in two-dimensional data by generating a two-dimensional histogram characterized by a grid of bins, determining a density estimate based on the bins, and identifying at least one cluster in the data. A smoothed density estimate is generated using a Gaussian kernel estimator algorithm. Clusters are identified by locating peaks and valleys in the density estimate (e.g., by comparing slope of adjacent bins). Boundaries (e.g., polygons) around clusters are identified using bins after bins are identified as being associated with a cluster. Boundaries can be simplified (e.g., by reducing the number of vertices in a polygon) to facilitate data manipulation.

Abstract: A data processing circuit adapted for use in pattern matching between two sets of multi-dimensional signal data. The data processing circuit performs integration-based conversion on data aw calculated by multiplying first multi-dimensional signal data a by a window function w, second multi-dimensional signal data b, data b.sup.2 calculated by squaring the data b, and the window function w, calculates a correlation between the first and second multi-dimensional signal data items a and b on the basis of the data aw and data b subjected to integration-based conversion, calculates a means of deviations from the square of the second multi-dimensional signal data b on the basis of the data b.sup.2 and window function subjected to integration-based conversion, and calculates a portion of the second multi-dimensional signal data b most consistent with the first multi-dimensional signal data a multiplied by the window function w.