Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. An example apparatus includes at least one memory; at least one processor to execute the computer readable instructions to at least: determine a first amplitude value of a frequency component in a frequency spectrum of an audio sample; determine a set of points in the frequency spectrum having at least one of (a) amplitude values within an amplitude threshold of the first amplitude value, (b) frequency values within a frequency threshold of the first amplitude value, or (c) phase values within a phase threshold of the first amplitude value; increment a counter when a distance between (1) a second amplitude value in the set of points and (2) the first amplitude value satisfies a distance threshold; and when the counter satisfies a counter threshold, generate a contour trace based on the set of points.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. An example apparatus includes at least one memory; at least one processor to execute the computer readable instructions to at least: determine a first amplitude value of a frequency component in a frequency spectrum of an audio sample; determine a set of points in the frequency spectrum having at least one of (a) amplitude values within an amplitude threshold of the first amplitude value, (b) frequency values within a frequency threshold of the first amplitude value, or (c) phase values within a phase threshold of the first amplitude value; increment a counter when a distance between (1) a second amplitude value in the set of points and (2) the first amplitude value satisfies a distance threshold; and when the counter satisfies a counter threshold, generate a contour trace based on the set of points.

Abstract: Methods, apparatus, and systems are disclosed to segment audio and determine audio segment similarities. An example apparatus includes at least one memory storing instructions and processor circuitry to execute instructions to at least select an anchor index beat of digital audio, identify a first segment of the digital audio based on the anchor index beat to analyze, the first segment having at least two beats and a respective center beat, concatenate time-frequency data of the at least two beats and the respective center beat to form a matrix of the first segment, generate a first deep feature based on the first segment, the first deep feature indicative of a descriptor of the digital audio, and train internal coefficients to classify the first deep feature as similar to a second deep feature based on the descriptor of the first deep feature and a descriptor of a second deep feature.

Abstract: Methods, apparatus, and systems are disclosed to segment audio and determine audio segment similarities. An example apparatus includes at least one memory storing instructions and processor circuitry to execute instructions to at least select an anchor index beat of digital audio, identify a first segment of the digital audio based on the anchor index beat to analyze, the first segment having at least two beats and a respective center beat, concatenate time-frequency data of the at least two beats and the respective center beat to form a matrix of the first segment, generate a first deep feature based on the first segment, the first deep feature indicative of a descriptor of the digital audio, and train internal coefficients to classify the first deep feature as similar to a second deep feature based on the descriptor of the first deep feature and a descriptor of a second deep feature.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. An example apparatus includes at least one memory; at least one processor to execute the computer readable instructions to at least: determine a first amplitude value of a frequency component in a frequency spectrum of an audio sample; determine a set of points in the frequency spectrum having at least one of (a) amplitude values within an amplitude threshold of the first amplitude value, (b) frequency values within a frequency threshold of the first amplitude value, or (c) phase values within a phase threshold of the first amplitude value; increment a counter when a distance between (1) a second amplitude value in the set of points and (2) the first amplitude value satisfies a distance threshold; and when the counter satisfies a counter threshold, generate a contour trace based on the set of points.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal; an EQ filter settings analyzer to: access a plurality of audio playback settings determined by the neural network based on the query; and determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; an EQ personalization manager to; generate a personalized EQ setting; and an EQ adjustment implementor to: blend the personalized EQ setting and the filter coefficient to generate a blended equalization; and apply the blended equalization to the audio signal in a first duration.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to adjust audio playback settings based on analysis of audio characteristics. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal; an EQ filter settings analyzer to: access a plurality of audio playback settings determined by the neural network based on the query; and determine a filter coefficient to apply to the audio signal based on the plurality of audio playback settings; and an EQ adjustment implementer to apply the filter coefficient to the audio signal in a first duration.

Abstract: Methods, apparatus, and systems are disclosed to segment audio and determine audio segment similarities. An example apparatus includes at least one memory storing instructions and processor circuitry to execute instructions to at least select an anchor index beat of digital audio, identify a first segment of the digital audio based on the anchor index beat to analyze, the first segment having at least two beats and a respective center beat, concatenate time-frequency data of the at least two beats and the respective center beat to form a matrix of the first segment, generate a first deep feature based on the first segment, the first deep feature indicative of a descriptor of the digital audio, and train internal coefficients to classify the first deep feature as similar to a second deep feature based on the descriptor of the first deep feature and a descriptor of a second deep feature.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. An example apparatus includes at least one memory; at least one processor to execute the computer readable instructions to at least: determine a first amplitude value of a frequency component in a frequency spectrum of an audio sample; determine a set of points in the frequency spectrum having at least one of (a) amplitude values within an amplitude threshold of the first amplitude value, (b) frequency values within a frequency threshold of the first amplitude value, or (c) phase values within a phase threshold of the first amplitude value; increment a counter when a distance between (1) a second amplitude value in the set of points and (2) the first amplitude value satisfies a distance threshold; and when the counter satisfies a counter threshold, generate a contour trace based on the set of points.

Abstract: Methods, apparatus, systems and articles of manufacture to segment audio and determine audio segment similarities are disclosed. A disclosed example method includes developing features characterizing audio with a neural network, computing a self-similarity matrix based on the features, and identifying segments of the audio based on the self-similarity matrix.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. An example apparatus includes a contour tracer to determine a first point of comparatively large amplitude of a frequency component in a frequency spectrum of an audio sample, determine a set of points in the frequency spectrum having amplitude values within an amplitude threshold of the first point, frequency values within a frequency threshold of the first point, and phase values within a phase threshold of the first point, increment a counter when a distance between (1) a second point in the set of points and (2) the first point satisfies a distance threshold, and when the counter satisfies a counter threshold, generate the contour trace, the contour trace including the set of points, and a subtractor to remove the contour trace from the audio sample when the amplitude values satisfy an outlier threshold.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. An example apparatus includes a contour tracer to determine a first point of comparatively large amplitude of a frequency component in a frequency spectrum of an audio sample, determine a set of points in the frequency spectrum having amplitude values within an amplitude threshold of the first point, frequency values within a frequency threshold of the first point, and phase values within a phase threshold of the first point, increment a counter when a distance between (1) a second point in the set of points and (2) the first point satisfies a distance threshold, and when the counter satisfies a counter threshold, generate the contour trace, the contour trace including the set of points, and a subtractor to remove the contour trace from the audio sample when the amplitude values satisfy an outlier threshold.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. Example methods disclosed herein include determining a point of comparatively large amplitude for a frequency in an audio sample, generating a contour trace beginning at the point, and calculating a parameter for the contour trace. The example method includes, in response to the contour trace being an outlier based on the parameter, removing the contour trace from the audio sample.

Abstract: Methods, apparatus, systems and articles of manufacture to segment audio and determine audio segment similarities are disclosed. A disclosed example method includes developing features characterizing audio with a neural network, computing a self-similarity matrix based on the features, and identifying segments of the audio based on the self-similarity matrix.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. Example methods disclosed herein include determining a point of comparatively large amplitude for a frequency in an audio sample, generating a contour trace beginning at the point, and calculating a parameter for the contour trace. The example method includes, in response to the contour trace being an outlier based on the parameter, removing the contour trace from the audio sample.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to reduce noise from harmonic noise sources. Example methods disclosed herein include determining a first point representing a comparatively large amplitude for a frequency value in an audio sample. Disclosed example methods also include generating a first contour trace of points having amplitude, frequency and phase values within thresholds from the first point of comparatively large amplitude. Example methods include generating a second contour trace of points having amplitude, frequency and phase values within thresholds of a second point of comparatively large amplitude. Disclosed example methods include calculating a parameter for each of the contour traces and determining if the first or second contour traces represent outliers based on the parameters. Disclosed example methods also include, in response to determining the contour to be an outlier contour trace, removing the outlier contour trace from the audio sample.

Abstract: A method for applying a quality score to digital content automatically, based on how people naturally use and interact with it. Using an adjustable algorithm, the method tracks interactions, and automatically applies a score based on those interactions. The score is calculated and continuously adjusted based on the interactions of all people who use interact with the digital content.