Abstract: A cover song identification method implemented by a computing system comprises receiving, by a computing system and from a user device, harmonic pitch class profile (HPCP) information that specifies one or more HPCP features associated with target audio content. A major chord profile feature and a minor chord profile feature associated with the target audio content are derived from the HPCP features. Machine learning logic of the computing system determines, based on the major chord profile feature and the minor chord profile feature, a relatedness between the target audio content and each of a plurality of audio content items specified in records of a database. Each audio content item is associated with cover song information. Cover song information associated with an audio content item having a highest relatedness to the target audio content is communicated to the user device.

Abstract: In one aspect, an example method includes (i) determining, by a playback device, a first loudness level of a first portion of first media content from a first source while the playback device presents the first media content, with the first portion having a first length; (ii) switching, by the playback device, from presenting the first media content from the first source to presenting second media content from a second source; (iii) based on the switching, determining, by the playback device, second loudness levels of second portions of the first media content while the playback device presents the second media content, with the second portions having a second length that is shorter than the first length; and (iv) while the playback device presents the second media content, adjusting, by the playback device, a volume of the playback device based on one or more of the second loudness levels.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization based on variant selection. An example apparatus to generate equalization adjustments for an audio signal includes an input feature set generator to generate an equalization input feature set by generating a data structure corresponding to a user input, the data structure including a number of entries identifying a selected variant of music and including the data structure in the equalization input feature set, and a model executor to, in response to obtaining the equalization input feature set, adjust at least one weight of a neural network model to generate the equalization adjustments for the audio signal.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for efficient media indexing. An example method disclosed herein includes means for initiating a list of hash seeds, the list of hash seeds including at least a first hash seed value and a second hash seed value among other hash seed values, means for generating to generate a first bucket distribution based on the first hash seed value and a first hash function and generate a second bucket distribution based on the second hash seed value used in combination with the first hash seed value, means for determining to determine a first entropy value of the first bucket distribution, wherein data associated with the first bucket distribution is stored in a first hash table and determine a second entropy value of the second bucket distribution.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to identify media. An example method includes: in response to a query, generating an adjusted sample media fingerprint by applying an adjustment to a sample media fingerprint; comparing the adjusted sample media fingerprint to a reference media fingerprint; and in response to the adjusted sample media fingerprint matching the reference media fingerprint, transmitting information associated with the reference media fingerprint and the adjustment.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for efficient media indexing. An example method disclosed herein includes selecting a first hash seed value based on a first entropy value calculated for a first bucket distribution resulting from use of the first hash seed value to store data in a first hash table, selecting a second hash seed value to be used in combination with the first hash seed value based on a second entropy value calculated on a second bucket distribution resulting from use of the first hash seed value in combination with the second hash seed value, and storing data in the first hash table based on the first hash seed value and a second hash table based on the second hash seed value.

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 generate light control information. An example apparatus comprises a beat tracking network to determine a length of time between a first media onset and a second media onset, a light drive waveform generator to compare the length of time to a time threshold, the time threshold corresponding to a desired time between consecutive light pulses, when the time threshold is not satisfied, increase the length of time, the increased length of time corresponding to light pulse spacing, and generate light control information based on the light pulse spacing, the light control information to cause the consecutive light pulses, and an effect engine to generate intensity information based on a first amplitude of the first media onset and a second amplitude of the second media onset, an amplitude of the consecutive light pulses corresponding to the intensity information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification.

Abstract: In one aspect, an example method includes (i) extracting a sequence of audio features from a portion of a sequence of media content; (ii) extracting a sequence of video features from the portion of the sequence of media content; (iii) providing the sequence of audio features and the sequence of video features as an input to a transition detector neural network that is configured to classify whether or not a given input includes a transition between different content segments; (iv) obtaining from the transition detector neural network classification data corresponding to the input; (v) determining that the classification data is indicative of a transition between different content segments; and (vi) based on determining that the classification data is indicative of a transition between different content segments, outputting transition data indicating that the portion of the sequence of media content includes a transition between different content segments.

Abstract: In one aspect, an example method includes (i) determining, by a playback device, a loudness level of first media content that the playback device is receiving from a first source; (ii) comparing, by the playback device, the determined loudness level of the first media content with a reference loudness level indicated by a loudness level profile for the first media content; (iii) determining, by the playback device, a target volume level for the playback device based on a difference between the determined loudness level of the first media content and the reference loudness level; and (iv) while the playback device presents second media content from a second source in place of the first media content, adjusting, by the playback device, a volume of the playback device toward the target volume level.

Abstract: In one aspect, an example method includes (i) determining, by a playback device, a first loudness level of a first portion of first media content from a first source while the playback device presents the first media content, with the first portion having a first length; (ii) switching, by the playback device, from presenting the first media content from the first source to presenting second media content from a second source; (iii) based on the switching, determining, by the playback device, second loudness levels of second portions of the first media content while the playback device presents the second media content, with the second portions having a second length that is shorter than the first length; and (iv) while the playback device presents the second media content, adjusting, by the playback device, a volume of the playback device based on one or more of the second loudness levels.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification. Examples methods include analyzing, with a neural network trained model, a parameter of an audio signal associated with a first volume level to determine a classification group associated with the audio signal, determining an input volume of the audio signal, the selection based on the classification group associated with the audio signal, applying a gain value to the audio signal, the gain value based on the classification group and the input volume, the gain value to modify the first volume level to a second volume level, and applying a compression value to the audio signal, the compression value to modify the second volume level to a third volume level that satisfies a target volume threshold.

Abstract: As a user is being presented with interactive media by a presenting device, a separate monitoring device may be used to monitor the presentation of the interactive media and detect an event that occurs therein. Such a monitoring device may be configured and positioned to access media content from the presentation of the interactive media. For example, the monitoring device may be configured and positioned to record video content with a camera and record audio content with a microphone. Having accessed this media content, the monitoring device may generate an identifier, such as a fingerprint or watermark, of the media content and compare the generated identifier with a reference identifier that is generated from the source of the media content. Based on the generated identifier matching the reference identifier, the monitoring device may detect that an event has occurred within the interactive media presentation and present a corresponding notification.

Abstract: In one aspect, an example method includes (i) determining, by a playback device, a loudness level of first media content that the playback device is receiving from a first source; (ii) comparing, by the playback device, the determined loudness level of the first media content with a reference loudness level indicated by a loudness level profile for the first media content; (iii) determining, by the playback device, a target volume level for the playback device based on a difference between the determined loudness level of the first media content and the reference loudness level; and (iv) while the playback device presents second media content from a second source in place of the first media content, adjusting, by the playback device, a volume of the playback device toward the target volume level.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to adjust device control information. The example apparatus comprises a light drive waveform generator to obtain metadata corresponding to media and generate device control information based on the metadata, the device control information to inform a lighting device to enable consecutive light pulses; an effect engine to apply an attack parameter and a decay parameter to consecutive light pulses corresponding to the device control information, the attack parameter and the decay parameter based on the metadata to affect a shape of the consecutive light pulses; and a color timeline generator to generate color information based on the metadata, the color information to inform the lighting device to change a color state.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example instructions disclosed herein cause one or more processors to at least: detect an irregularity in a frequency representation of an audio signal in response to a change in volume between a set of frequency values exceeding a threshold; and adjust a volume at a first frequency value of the set of frequency values to reduce the irregularity.

Abstract: In one aspect, an example method includes (i) determining, by a playback device, a first loudness level of a first portion of first media content from a first source while the playback device presents the first media content, with the first portion having a first length; (ii) switching, by the playback device, from presenting the first media content from the first source to presenting second media content from a second source; (iii) based on the switching, determining, by the playback device, second loudness levels of second portions of the first media content while the playback device presents the second media content, with the second portions having a second length that is shorter than the first length; and (iv) while the playback device presents the second media content, adjusting, by the playback device, a volume of the playback device based on one or more of the second loudness levels.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to select reference sub-fingerprints for comparison to query sub-fingerprints based on a determination that a query sub-fingerprint is a match with a reference sub-fingerprint, generate a count vector that stores total counts of matches between the query sub-fingerprints and different subsets of the reference sub-fingerprints, each of the different subsets being aligned to the query sub-fingerprints at a different offset from a reference point, each of the different offsets being mapped by the count vector to a different total count, calculate a maximum count among the total counts, a median of the total counts, and a difference between the maximum count and the median of the total counts, and classify the reference sub-fingerprints as a match with the query sub-fingerprints based on the difference between the maximum count in the count vector and the median.