Predicting acoustic features for geographic locations

A system comprising:

• a prediction engine comprising one or more processing devices, the prediction engine configured to:
• receive identification information associated with a geographic location, the identification information comprising one or more features that affect an acoustic environment of the geographic location at a particular time,
• determine one or more parameters representing at least a subset of the one or more features,
• estimate, based on the one or more parameters, at least one acoustic parameter that represents the acoustic environment of the geographic location at the particular time, wherein the at least one acoustic parameter is estimated using a mapping function that generates the estimate of the at least one acoustic parameter as a weighted combination of the one or more parameters, wherein the mapping function associated with the weighted combination is determined using a machine-learning process based on training information that includes at least one of:
• (i) time-specific information and (ii) location-specific information associated with ambient acoustic features at a plurality of geographic locations, and a measure of acoustic environment for each of the plurality of geographic location, wherein training of the machine-learning process comprises:
• accessing the training information,
• identifying, from at least a portion of the training information, a set of input parameters each for at least a subset of the plurality of geographic locations, and
• determining the mapping function based on (i) the sets of input parameters and (ii) the corresponding measures of acoustic environments, such that each set of input parameters, when combined in a weighted combination in accordance with the mapping function, maps to the corresponding measure of acoustic environment;
• and
• cause a presentation of information representing the at least one acoustic parameter estimated for the geographic location for the particular time, wherein the information representing the at least one acoustic parameter is presented on a user interface displayed on a computing device;
• and
• a storage device configured to store a representation of the mapping function.

One or more non-transitory machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processors to perform operations comprising:

• receiving identification information associated with a geographic location, the identification information comprising one or more features that affect an acoustic environment of the geographic location at a particular time;
• determining one or more parameters representing at least a subset of the one or more features;
• estimating based on the one or more parameters, at least one acoustic parameter that represents the acoustic environment of the geographic location at the particular time, wherein the at least one acoustic parameter is estimated using a mapping function that generates the estimate of the at least one acoustic parameter as a weighted combination of the one or more parameters, wherein the mapping function associated with the weighted combination is determined using a machine-learning process based on training information that includes at least one of:
• (i) time-specific information and (ii) location-specific information associated with ambient acoustic features at a plurality of geographic locations, and a measure of acoustic environment for each of the plurality of geographic location;
• and
• causing a presentation of information representing the at least one acoustic parameter estimated for the geographic location for the particular time, wherein the information representing the at least one acoustic parameter is presented on a user interface displayed on a computing device,
• wherein training of the machine-learning process comprises:
• accessing the training information,
• identifying, from at least a portion of the training information, a set of input parameters each for at least a subset of the plurality of geographic locations, and
• determining the mapping function based on (i) the sets of input parameters and (ii) the corresponding measures of acoustic environments, such that each set of input parameters, when combined in a weighted combination in accordance with the mapping function, maps to the corresponding measure of acoustic environment.