Patent ID: 12192714

DETAILED DESCRIPTION

The illustrative embodiments recognize and take into account one or more different considerations as described herein. For airlines to comply with these requirements, airlines need to be able to accurately predict noise for their arriving and departing flights. Additionally, air navigation service providers need be able to accurately predict noise for flights so that the air navigation service providers can generate noise-reduced arrival and departure procedures and enforce noise mitigated arrivals and departures.

Being able to accurately predict the maximum sound pressure level for an arriving or departing flight in the vicinity of airport can be used to improve the impact of aircraft noise on the environment for locations around the airport. Further, with ability to predict maximum sound pressure levels (LAmax) for arriving and departing flights, airlines can plan and use flight paths that have maximum sound pressure levels that are lower to avoid penalties imposed by air navigation service providers. Further, with the ability to accurately predict maximum sound pressure levels, air navigation service providers can generate noise reduced arrival and departure procedures.

Currently, various approaches are present for predicting noise for flights. One approach is the FAA Aviation Environmental Design Tool (AEDT). This tool uses the noise-power distance (NPD) curves that provide noise levels measured at various distances from a particular aircraft and engine configuration for different thrust settings and operational modes.

Although this approach is relatively numerically efficient and consistent with noise certification values, this approach cannot capture noise propagation effects for different weather conditions and aircraft configurations. As a result, the accuracy of AEDT is not as great as desired.

Another approach is a semi-empirical, physics-based approximation model employed by the NASA Aircraft Noise Prediction Program (ANOPP). This model computes noise levels from the airframe and engine components at a user-defined three-dimensional observer grid for an arbitrary flight procedure. This model can be continuously updated with historical noise data and applies parametric and component models to separately simulate each relevant noise source.

Although this approach is relatively accurate, it is time consuming and computer-intensive. This approach also suffers from lack of robust validation for each relevant noise source and several simplifications. For example, wind was ignored. ANOPP is a semi-empirical and physics-based system in which human operators formulate the solution using only several parameters for a limited number of cases.

Thus, illustrative examples employ deep learning models in the form of deep learning models that are unbiased and can utilize large amounts of data. These machines learning models can also consider all regular and irregular edge cases formulating a solution with as many parameters that are available. The illustrative examples provide a more accurate and efficient manner to predict maximum sound pressure levels through the use of deep learning models as compared to current techniques for predicting maximum sound pressure levels. This prediction can be performed in real time as an aircraft flies on a flight path over a location such as an airport. With this increased accuracy in predicting maximum sound pressure levels using deep learning models, adjustments or changes to the flight paths of aircraft can be made to reduce or avoid generating maximum sound pressure levels that are greater than specified thresholds for the flight path over the airport or areas surrounding the airport.

With reference now to the figures and, in particular, with reference toFIG.1, a pictorial representation of a network of data processing systems is depicted in which illustrative embodiments can be implemented. Network data processing system100is a network of computers in which the illustrative embodiments may be implemented. Network data processing system100contains network102, which is the medium used to provide communications links between various devices and computers connected together within network data processing system100. Network102may include connections, such as wire, wireless communication links, or fiber optic cables.

In the depicted example, server computer104and server computer106connect to network102along with storage unit108. In addition, client devices110connect to network102. As depicted, client devices110include client computer112, client computer114, and client computer116. Client devices110can be, for example, computers, workstations, or network computers. In the depicted example, server computer104provides information, such as boot files, operating system images, and applications to client devices110. Further, client devices110can also include other types of client devices such as mobile phone118, tablet computer120, and smart glasses122. In this illustrative example, server computer104, server computer106, storage unit108, and client devices110are network devices that connect to network102in which network102is the communications media for these network devices. Some or all of client devices110may form an Internet of things (IoT) in which these physical devices can connect to network102and exchange information with each other over network102.

Client devices110are clients to server computer104in this example. Network data processing system100may include additional server computers, client computers, and other devices not shown. Client devices110connect to network102utilizing at least one of wired, optical fiber, or wireless connections.

Program instructions located in network data processing system100can be stored on a computer-recordable storage media and downloaded to a data processing system or other device for use. For example, program instructions can be stored on a computer-recordable storage media on server computer104and downloaded to client devices110over network102for use on client devices110.

In the depicted example, network data processing system100is the Internet with network102representing a worldwide collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) suite of protocols to communicate with one another. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes or host computers consisting of thousands of commercial, governmental, educational, and other computer systems that route data and messages. Of course, network data processing system100also may be implemented using a number of different types of networks. For example, network102can be comprised of at least one of the Internet, an intranet, a local area network (LAN), a metropolitan area network (MAN), or a wide area network (WAN).FIG.1is intended as an example, and not as an architectural limitation for the different illustrative embodiments.

As used herein, “a number of” when used with reference to items, means one or more items. For example, “a number of different types of networks” is one or more different types of networks.

Further, the phrase “at least one of,” when used with a list of items, means different combinations of one or more of the listed items can be used, and only one of each item in the list may be needed. In other words, “at least one of” means any combination of items and number of items may be used from the list, but not all of the items in the list are required. The item can be a particular object, a thing, or a category.

For example, without limitation, “at least one of item A, item B, or item C” may include item A, item A and item B, or item B. This example also may include item A, item B, and item C or item B and item C. Of course, any combinations of these items can be present. In some illustrative examples, “at least one of” can be, for example, without limitation, two of item A; one of item B; and ten of item C; four of item B and seven of item C; or other suitable combinations.

This illustrative example, client computer112is a client device in client devices110located at or in communication with airport130. As depicted, commercial airplane132can arrive and takeoff from airport130. Commercial airplane132generates noise that can be measured using as sequential LAmax134. In the illustrative example, sequential LAmax134is sequence of maximum sound pressure levels that commercial airplane132generates during flight over airport130. This flight can be for an arriving flight or a departing flight from airport130in this illustrative example.

In this illustrative example, deep learning system136comprises deep learning model138that operates to predict sequential LAmax134in real time as commercial airplane132flies over airport130when arriving or taking off. For example, deep learning model138can predict a sequence of LAmax values133for sequential LAmax134for a flight path being flown by commercial airplane132.

For example, with a recorded sequence of LAmax values133for sequential LAmax134for the portion of the flight path that commercial airplane132has already flown, deep learning model138in deep learning system136can predict a sequence of LAmax values for sequential LAmax134that is predicted to occur when commercial airplane132flies on an unflown portion of the flight path of commercial airplane132. In other words, deep learning system136can predict sequential LAmax134for the unflown portion of the flight path for commercial airplane132using sequential LAmax134that has been recorded for the flown portion of the flight path in real time as the flight of commercial airplane132occurs.

In this illustrative example, deep learning model138has been trained to predict noise in the form of a sequence of LAmax values using a training dataset that includes historical aircraft sensor data, historical atmospheric data, and historical sound data.

With reference now toFIG.2, a block diagram of a noise environment is depicted in accordance with an illustrative embodiment. In this illustrative example, noise environment200includes components that can be implemented in hardware such as the hardware shown in network data processing system100inFIG.1.

In this illustrative example, deep learning system202in noise environment200can operate to predict noise204for aircraft206. In this illustrative example, the prediction of noise204is a prediction of sequential maximum sound pressure levels208generated by aircraft206. In this example, sequential maximum sound pressure levels208can be referred to as sequential LAmax210. Sequential maximum sound pressure levels208can be a maximum A-weighted sound pressure levels in which the A weighting adjusts sound pressure reading levels to reflect the sensitivity of the human ear.

Aircraft206can take a number of different forms. For example, aircraft206can be a commercial airplane, a tilt-rotor aircraft, a tilt wing aircraft, a vertical takeoff and landing aircraft, an electrical vertical takeoff and landing vehicle, a personal air vehicle, and other types of aircraft206.

In this illustrative example, deep learning system136comprises computer system212and controller214. Controller214is located in computer system212. In this illustrative example, controller214can operate as a maximum sound pressure level predictor to predict sequential maximum sound pressure levels208generated by generated by aircraft206for flight path216over location218. In this illustrative example, flight path216can be for an arrival or departure of aircraft206. Location218is an airport in this example but can be any area where a prediction of sequential maximum sound pressure levels208is of interest. As another example, location218can be an area adjacent to or near an airport.

Controller214can be implemented in software, hardware, firmware, or a combination thereof. When software is used, the operations performed by controller214can be implemented in program code configured to run on hardware, such as a processor unit. When firmware is used, the operations performed by controller214can be implemented in program code and data and stored in persistent memory to run on a processor unit. When hardware is employed, the hardware can include circuits that operate to perform the operations in controller214.

In the illustrative examples, the hardware can take a form selected from at least one of a circuit system, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device, or some other suitable type of hardware configured to perform a number of operations. With a programmable logic device, the device can be configured to perform the number of operations. The device can be reconfigured at a later time or can be permanently configured to perform the number of operations. Programmable logic devices include, for example, a programmable logic array, a programmable array logic, a field programmable logic array, a field programmable gate array, and other suitable hardware devices. Additionally, the processes can be implemented in organic components integrated with inorganic components and can be comprised entirely of organic components excluding a human being. For example, the processes can be implemented as circuits in organic semiconductors.

Computer system212is a physical hardware system and includes one or more data processing systems. When more than one data processing system is present in computer system212, those data processing systems are in communication with each other using a communications medium. The communications medium can be a network. The data processing systems can be selected from at least one of a computer, a server computer, a tablet computer, or some other suitable data processing system.

As depicted, computer system212includes a number of processor units220that are capable of executing program instructions222implementing processes in the illustrative examples. As used herein a processor unit in the number of processor units220is a hardware device and is comprised of hardware circuits such as those on an integrated circuit that respond and process instructions and program code that operate a computer. When a number of processor units220execute program instructions222for a process, the number of processor units220is one or more processor units that can be on the same computer or on different computers. In other words, the process can be distributed between processor units on the same or different computers in a computer system. Further, the number of processor units220can be of the same type or different type of processor units. For example, a number of processor units can be selected from at least one of a single core processor, a dual-core processor, a multi-processor core, a general-purpose central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), or some other type of processor unit.

In this illustrative example, controller214trains a set of deep learning models224in the form of a set of deep learning models224to predict sequential maximum sound pressure levels208generated by aircraft206for flight path216during flight217of aircraft206over location218using a training dataset226. In this depicted example, flight path216is being flown by aircraft206when the set of deep learning models224makes predictions of sequential maximum sound pressure levels208.

In this illustrative example, training dataset226comprises historical aircraft sensor data228for selected parameters230, historical atmospheric data232, and historical sound data234recorded by microphone system236for flight paths238over location218. Historical aircraft sensor data228for selected parameters230, historical atmospheric data232, and historical sound data234recorded by microphone system236for flight paths238can be for at least one of an aircraft type, a tail number, or an airline. In other words, training dataset226can have levels of granularity with respect to predictions of sequential maximum sound pressure levels208. In other words, the historical data in training dataset226can be obtained using a number of different aircraft

Historical aircraft sensor data228is sensor generated data from various aircraft flying flight paths238over location218. The various aircraft that generate historical aircraft sensor data228can include aircraft206or other aircraft. Historical atmospheric data232can include temperature, humidity, pressure, wind, and other weather conditions. Historical atmospheric data232can include information for different altitudes.

In this illustrative example, historical sound data234comprises sound data recorded for flight paths238. This sound data can be, for example, historical maximum sound pressure levels239. Historical sound data234can also include metadata about the sound recorded. For example, the metadata can include slant distances, microphone configuration, timestamps, microphone locations, and other suitable information. Microphone system236comprises microphones237at different positions in or near location218. These microphones can record sound in location218. For example, microphones237can be located along a runway, a flight path, or other positions and location218. In this example, microphones237can be located along flight paths238and record sound generated by different aircraft flying on flight paths238.

In this illustrative example, all of the historical data in training dataset226is correlated to times for when flight paths238are already flown flight paths for one or more aircraft. Historical sound data234can be part of airport data240. Further, airport data240can include other information for training dataset226. For example, airport data240can include information about aircraft altitude, location, and other information that can be used to determine flight paths238. As a result, flight paths238can be obtained from airport data240in addition to or in place of using historical aircraft sensor data228.

As depicted, controller214can predict sequential maximum sound pressure levels208generated by aircraft206for flight217of aircraft206using flight path216over location218using the set of deep learning models224after training the set of deep learning models224using training dataset226. The prediction of sequential maximum sound pressure levels208generated by aircraft206for flight217of aircraft206using flight path216over location218can be performed using the set of deep learning models224. This prediction is performed in real time in the illustrative examples.

In other words, the prediction is made while aircraft206is flying using flight path216. Further, this prediction can be made in a timely manner for performing actions242such as to control or change flight path216such that the maximum sequential pressure levels predicted can change to be within thresholds required for location218.

For example, controller214identifies a first set of the sequential maximum sound pressure levels208recorded by a first consecutive set of the microphones237along flight path216during flight217of aircraft206using flight path216. The first set of the sequential maximum sound pressure levels208recorded by a first consecutive set of the microphones237can be received in sound data245from microphone system236. Sound data245can be part of airport data.

Controller214can predict a second set of the sequential maximum sound pressure levels208that will be recorded by a second consecutive set of the microphones237along flight path216during flight217of aircraft206using flight path216over location218. In this example, the second set of the sequential maximum sound pressure levels208is predicted using the set of deep learning models224after training the set of deep learning models224using the training dataset.

In this depicted example, a first consecutive set of the microphones237in microphone system236records the first set of the sequential maximum sound pressure levels208from when aircraft206flew on first portion241of flight path216. This first set of the sequential maximum sound pressure levels208is used as an input into the set of deep learning models224. In this example, the set of deep learning models224is a single deep learning model.

In response, the set of deep learning models224generates an output in the form of a prediction of the second set of the sequential maximum sound pressure levels208that will be recorded by a second consecutive set of the microphones237along the flight path216. In this depicted example, the second set of the sequential maximum sound pressure levels208is for second portion243of flight path216that has not yet been flown by aircraft206.

In other words, first portion241has already been flown by aircraft206while second portion243is has yet to be flown by aircraft206. In this manner, a real time prediction of the second set of the sequential maximum sound pressure levels208can be made based on the first set of the sequential maximum sound pressure levels208already generated by aircraft206and recorded by first consecutive set of the microphones237.

Further, in this example the first consecutive set of the microphones237comprises a series of microphones237along first portion241of flight path216without a gap in microphones237or skipping a microphone in microphones237. In similar fashion, the second consecutive set of the microphones237is also a series of microphones237along second portion243of flight path216without a gap or skipping a microphone in microphones237.

In this illustrative example, based on predicting sequential maximum sound pressure levels208, controller214can perform a set of actions242. The set of actions242can include at least one of planning flight path216over location218using the prediction of the sequential maximum sound pressure levels208generated by aircraft206for flight path216over location218, determining compliance with a regulation regarding sequential maximum sound pressure levels208for this flight over location218, changing second portion243of flight path216, identifying changes to aircraft configuration for aircraft206for flying second portion243of flight path216, or other suitable actions.

These actions can be used to reduce noise204generated by aircraft206in real time while aircraft206flies over location218using flight path216. For example, actions242can include changing at least one of a thrust level, control surface position, a flap position, a spoiler position, or some configuration for aircraft206. In another example the actions242can include changing one or more waypoints for flight path216being flown by aircraft206. Further, the set of actions242can be used for other purposes in addition to or in place of controlling flight path216during flight217of aircraft206.

For example, the set of actions242can be used by aviation authorities and airlines to plan or replan individual flight paths. As another example, the set of actions242can be used by aviation authorities and airlines to plan or replan cumulative flight paths. This replanning can be for both arrival and departure of flights and made in a manner to reduce noise204for location218and surrounding locations. In this manner, concerns with noise pollution can be reduced using predictions of sequential maximum sound pressure levels208made by the set of deep learning models224.

Turning next toFIG.3, an illustration of a block diagram for training deep learning models is depicted in accordance with an illustrative embodiment. In this illustrative example, controller214receives raw data300from data sources302for processing to create training dataset226.

Raw data300can be large in volume, noisy, and incomplete. This raw data is historical data that can include continuous parameter logging (CPL) data, ERA5 (ECMWF Re-Analysis version 5) weather data, airport data, and other suitable types of data. In this example, continuous parameter logging data is an example of historical aircraft sensor data228inFIG.2.

In this example, ERA5 ECMWF Re-Analysis version 5) weather data is an example of historical atmospheric data232. ERA5 weather data can include vertical profiles of temperature, wind, humidity with timestamps. ERA5 is available from European Centre for Medium-Range Weather Forecasts (ECMWF), which is an independent intergovernmental organization.

Airport data in raw data300is an example of data that can include historical sound data234. This historical sound data234is ground truth and can include sequential maximum sound pressure levels208for microphone locations, slant distances, and aircraft altitudes. Sequential maximum sound pressure levels208can be referred to as sequential LAmax210.

In processing raw data300, controller214can perform feature engineering304. In this illustrative example, feature engineering304can include at least one of selecting relevant features, handling missing data, computing additional parameters, normalizing data, standardizing data, performing dimensionality reduction, or other suitable processing of the raw data. For example, controller214can compute additional parameters, such as thrust for left and right engines, atmospheric absorption along the flight path at 251 Hz, 398 Hz, and 631 Hz frequencies, day of week, and week of month, and impute values for missing parameters such as aircraft weight, fuel flow for left and right engines, runway code, and registration.

These different operations can be performed in feature engineering304to select salient features for use as selected parameters230for which data is included in training dataset226. In performing feature engineering, controller214can select parameters from parameters306identified in raw data300. The selection of these parameters are correlated parameters308that have a correlation to sequential maximum sound pressure levels208to form correlated parameters308.

Further, parameters306can be removed from correlated parameters308that are repetitive to form selected parameters230. Repetitive parameters can be filtered out using techniques such as correlation selection and mutual information. A final selection of salient features to form selected parameters230can be made using a dimensionality reduction algorithm such as Principal Component Analysis (PCA).

In this illustrative example, raw data300processed using feature engineering304forms processed data310. Processed data310can be stored in a data structure such as one or more tables.

With timeseries data in processed data310, each time step is a row and each sensor reading for a parameter is a column in the table. For example, each row can be for a particular time step in which the columns are values for selected parameters230.

Each table can be for a particular flight using the flight path for a particular microphone. For example, a table can be for departure or arrival using a flight path. In the illustrative example, a time step that best represents sequential maximum sound pressure levels208can be selected for a particular microphone.

In generating processed data310, controller214can select time instance326where smallest slant distance328is present between an aircraft and a microphone in which all of selected parameters230for time instance are stable parameters330. In this example, selected parameters230are stable parameters330when a moving average of each selected parameter in selected parameters230are within a threshold.

In illustrative example, selected parameters230are examined to identify stable parameters330and not all of the parameters from which selected parameters230were identified. Selected parameters230are correlated such that these parameters change over time as sequential maximum sound pressure levels208changes. Other parameters not identified as selected parameters230are excluded. These other parameters do not change or change regardless of changes to sequential maximum sound pressure levels208. For example, parameters such as date, time, or other similar parameters are not selected parameters230and are not considered.

When smallest slant distance328is found for a microphone, the selected parameters230for that time instance is added to training dataset226.

This selection can be repeated for each microphone in microphone system236for which historical sound data234is present. The selection can be performed using heuristics searching for the best matching instance where the slant distance microphone to the aircraft is below the threshold and selected parameters230remain stable. In this illustrative example, the maximum sound pressure level measured at the selected time instance is a label for selected parameters230for that time instance.

In this example, processed data310can be split or divided for use in evaluating quality of the set of deep learning models224. For example, k-fold cross validation312can be performed to divide up processed data for use. With k-fold cross validation312processed data310is split into k parts. k−1 part of the data is used for training and the remaining 1 part is used for validation and testing in a rotating manner.

In this illustrative example, training dataset226can be comprised of groups of data314. Each group of data in the groups of data314is for a flight using a flight path in the flights using flight paths238and includes historical aircraft sensor data for selected parameters230, historical atmospheric data232, and historical sound data234recorded by a microphone system corresponding to time instances for sequential maximum sound pressure levels208for the flight.

The set of deep learning models224can be single deep learning model316. In another example, the set of deep learning models224can be different deep learning models318.

Further, before, during or after training, weights324can be adjusted for different deep learning models318. In a deep learning model in deep learning models224, each selected parameter in selected parameters230in the deep learning model is assigned a different weight from other parameters in selected parameters230in the deep learning model in deep learning models224. In this example, different weights for parameters230can be selected initially by how one weight is more advantageous than the other weight. The selection can be performed to best fit the model to the problem so that the error is minimum, which is a difference between the actual and the predicted value.

In this illustrative example, the set of deep learning models224can use autoregression331when predictions of maximum sound pressure level are made sequentially to result in sequential maximum sound pressure level prediction333. With this type of prediction, the set of deep learning models224solve multi-step time series forecasting problem. In other words, the set of deep learning models224are trained such that m*maximum sound pressure level recorded along the flight path216by subsequent n*microphones is some function of observations at prior time steps.

In this example, the training using training dataset226is performed using unsupervised learning. In other words, the set of deep learning models224can discover patterns or data groupings in training dataset226without human intervention. For example, the set of deep learning models224learn from historical data and map independent variables in: historical aircraft sensor data228, historical atmospheric data232, historical sound data234, and airport data240to a dependent variable, maximum sound pressure level. These data points can each be mapped to the dependent variable. In other examples, labels can be added to training dataset when supervised learning is used.

In this illustrative example, the training is performed for each deep learning model in different deep learning models318. Each deep learning model is trained using the same training dataset, training dataset226. With this type of training, different deep learning models318can compete with each other to find the best deep learning model.

In other words, controller214can identify a deep learning model from different deep learning models318having a highest level of accuracy in predicting the maximum sound pressure levels. A deep learning model having the highest level of accuracy is selected deep learning model320and used to predict sequential maximum sound pressure levels208for flight path216of aircraft206over location218.

The selection of selected deep learning model320can be made in any number of different ways. For example, the resulting mean error and standard deviation can be determined for each of different deep learning models318to select the top ranking model as selected deep learning model320.

In the illustrative example, this training can be repeated periodically. In some cases, the training can be performed continuously as new raw data is received from data sources302. For example, new training datasets322can be generated from new aircraft sensor data for selected parameters230, new atmospheric data, and new sound data recorded by the microphone system236in raw data300.

With new training datasets322, training of different deep learning models318can be repeated. Further, identifying the deep learning model having the highest level of accuracy in predicting the maximum sound pressure levels can be performed in response to continuing to train different deep learning models318. As a result, selected deep learning model320can change over time as different deep learning models318are continued to be trained using new training datasets322.

With reference next toFIG.4, an illustration of a block diagram for predicting a maximum sound pressure level for a flight path of an aircraft is depicted in accordance with an illustrative embodiment. In this illustrative example, controller214can use the set of deep learning models to generate prediction400of sequential maximum sound pressure levels208for flight path216of aircraft206over location218.

In this illustrative example, controller214sends input402into the set of deep learning models224and in particular to selected deep learning model320. In response, the set of deep learning models224generates output406containing prediction400.

Input402is expected to be the same type of data in training dataset226without labels such as maximum sound pressure levels. In this illustrative example, input402comprises aircraft sensor data405for selected parameters230, atmospheric data408, and flight path216. Flight path216is path that aircraft206is using. Flight path216can be derived from airport data240or can be input as expected waypoints in aircraft sensor data405.

In this example, input402includes first set411of sequential maximum sound pressure levels208recorded by a first consecutive set of the microphones along the flight path216already flown by aircraft206using the flight path. This sound data can be received in airport data240.

At least one of aircraft sensor data405, airport data240, or atmospheric data408can be obtained for input402in real time. In this manner, predictions can be made for second set413of sequential maximum sound pressure levels208that will be recorded by a second consecutive set of the microphones237along flight path216during flight217of aircraft206using flight path216.

In this example, time series forecasting is performed by the set of deep learning models224in which first set411of sequential maximum sound pressure levels208recorded by the first consecutive set of the microphones237is for a portion of flight path216already flown by aircraft206. First set411of sequential maximum sound pressure levels208is a function of prior sequential time steps.

This input is sent into the set of deep learning models224and results in output406with prediction400. Prediction400contains second set413of sequential maximum sound pressure levels208as a function of subsequent sequential time steps for the portion of flight path216that has not yet been flown by aircraft206during the same flight.

In the illustrative examples, prediction400is generated in real time as aircraft206flies on flight path216to predict sequential maximum sound pressure levels208that will be recorded on flight path216based on sequential maximum sound pressure levels208already recorded on flight path216.

In this illustrative example, real time means the actual time during which an event occurs. For example, performing the prediction in real time can mean that inputs to the deep learning models are made quickly enough during the flight of aircraft on a flight path such that a prediction of sequential maximum sound pressure levels can be received immediately as feedback. In other words, the prediction can be received quickly enough such that actions can be taken or adjustments can be made to the flight of the aircraft using a particular flight path.

In one illustrative example, one or more technical solutions are present that overcome a problem with predicting maximum sound pressure levels for aircraft flying over location such as an airport with the desired level of accuracy. As a result, one or more solutions can provide a solution that employs deep learning models to predict aircraft noise more accurately as compared to current techniques. For example, deep learning system202can provide sequential maximum sound pressure levels208with a mean error of less than 2 dB(A) with a 0.2 dBA of standard deviation for both arrival and departures flights. This level of accuracy shows a higher level of improvement as compared to current aircraft noise prediction program (ANOPP2), which is an improved version of ANOPP that has a mean error of less than 4 dBA for departures for computing a maximum sound pressure level but has issues in computing this value for arrivals. The units dBA means the decibels measured are an expression of loudness of sounds in air as perceived by the human ear.

Computer system212can be configured to perform at least one of the steps, operations, or actions described in the different illustrative examples using software, hardware, firmware, or a combination thereof. As a result, computer system212operates as a special purpose computer system in which controller214in computer system212enables predictions of maximum sound pressure levels for flights of aircraft using different flight paths. In particular, controller214transforms computer system212into a special purpose computer system as compared to currently available general computer systems that do not have controller214.

The illustration of noise environment200and the different components in figures inFIGS.2-4is not meant to imply physical or architectural limitations to the manner in which an illustrative embodiment may be implemented. Other components in addition to or in place of the ones illustrated may be used. Some components may be unnecessary. Also, the blocks are presented to illustrate some functional components. One or more of these blocks may be combined, divided, or combined and divided into different blocks when implemented in an illustrative embodiment.

For example, controller214trains additional deep learning models to provide predictions of sequential maximum sound pressure levels208for other aircraft in addition to or in place of aircraft206. Further, computer system212can include components in aircraft206such that controller214can operate within aircraft206to predict sequential maximum sound pressure levels208in real time. With this location of components in aircraft206, controller214can more easily obtain sensor data from aircraft206in real time and make a real time prediction of sequential maximum sound pressure levels208for aircraft206.

Further, controller214may be located on a single computer or distributed in multiple computers in in computer system212. Additionally, the functionality of controller214can be implemented as a service in cloud computing environment231. This service can be microservice255in microservices233, which offered to aviation authorities, regulatory agencies, air navigation service providers, airlines, and other entities. Microservices233can be offered on a at least one of a subscription, a per use basis, or in some other manner.

Additionally, the predictions generated can also be used to develop aircraft designs that produce less noise. Further, these predictions can also be used to reconfigure, upgrade, or retrofit current aircraft to reduce noise produced by the aircraft.

Turning toFIG.5, an illustration for a deep learning model is depicted in accordance with an illustrative embodiment. As depicted, deep learning model500can take the form of neural network502. In this example, deep learning model500comprises input layer504, a set of hidden layers506, and output layer508.

In this example, the number of inputs507are in input layer504corresponds to the first set of the sequential maximum sound pressure levels208recorded by a first consecutive set of microphones237along first portion241of flight path216already flown by aircraft206. The number of outputs510in output layer508correspond to the second set of the sequential maximum sound pressure levels208that are predicted to be recorded by the second consecutive set of microphones237along second portion243of flight path216that will be flown by aircraft206.

In this example, each input corresponds to a microphone in the first consecutive set of microphones237and each output corresponds to a microphone in the second consecutive set of microphones237. The number of inputs507in input layer504and the number of outputs510in output layer508can be the same or different.

In other words, the number of microphones used as inputs can be different from the number of microphones used as outputs for which predictions are made for maximum sound pressure levels. For example, the number of inputs507can be 3 and the number of outputs510can be 4. In another example, number of inputs507can be 3 and the number of outputs510can be 3. In yet another example, the number of inputs507can be 1 and the number of outputs510can be 3. Further, first consecutive set of microphones237and second consecutive set of microphones237are consecutive to each other without a gap being present between the sets of microphones237.

As depicted hidden layers506can take a number of different forms. For example, hidden layers506can be selected from at least one of an encoder-decoder long short-term memory model, a convolutional neural network long short-term memory encoder decoder model, or a convolutional long short-term memory encoder-decoder model, or other suitable type of model.

In this example, deep learning model500uses autoregression for time series forecasting520. With this type of prediction, the first set of the sequential maximum sound pressure levels208recorded by the first consecutive set of the microphones237is a function of prior sequential time steps input into the deep learning model500and deep learning model500outputs the second set of sequential maximum sound pressure levels208as a function of subsequent sequential time steps.

With reference next toFIG.6, an illustration a block diagram of an encoder-decoder long short-term memory model is depicted in accordance with an illustrative embodiment. In this example, encoder-decoder long short-term memory model600is an example of one implementation for deep learning model500inFIG.5.

In this illustrative example, encoder-decoder long short-term memory model600has a number of different components. As depicted, encoder-decoder long short-term memory model600comprises input layer602, long short-term memory layer604, repeat vector606, long short-term memory layer608, time distributed layer610, and output layer612. Long short-term memory layer604, repeat vector606, long short-term memory layer608, and time distributed layer610are hidden layers506in encoder-decoder long short-term memory model600.

In this example, input layer602can have a number of inputs that receive values for recorded sequential maximum pressure levels that have been recorded for a first portion of a flight path that an aircraft has flown so far. These values are used in hidden layers506to predict future sequential maximum pressure levels that will occur for the aircraft based on the recorded sequential maximum pressure levels into input layer602. In this example, the future sequential maximum pressure levels are a second set of sequential maximum pressure levels that will be recorded as the aircraft flies a second portion of the flight path during the same flight.

The recorded sequential maximum pressure levels received at number of inputs in input layer602are sent to long short-term memory layer604in encoder-decoder long short-term memory model600. Long short-term memory layer604can comprise a set of long short-term memory layers and a set of neurons. For example, long short-term memory layer604can comprise a layer comprising 200 long short-term memory neurons. The long short-term memory neuron can also be referred to as a long short-term memory unit. The long short-term memory neuron is part of a recurrent neural network structure that comprises various components such as cells, an input gate, and an output gate. The long short-term memory neuron uses a chain structure that comprises neural networks and memory blocks established for short-term memory processes to create long-term memory.

In this illustrative example, long short-term memory layer604is an encoder of encoder-decoder long short-term memory model600. In this example, long short-term memory layer604receives the number of inputs from input layer602as an input sequency and reads the input sequency step-by-step. In this illustrative example, long short-term memory layer604learns the relationship between the steps in the input sequence of the number of inputs from input layer602and develops a learned internal representation of the input sequence of the number of inputs from input layer602as a fixed-length vector.

In this example, the fixed-length vector represents the learned internal representation of the input sequence by long short-term memory layer604. Further, in this illustrative example, the fixed-length vector is an output from long short-term memory layer604and is received by repeat vector606.

As depicted, repeat vector606is a layer in encoder-decoder long short-term memory model600. A repeat vector is a layer that transforms a fixed-length vector by repeating the fixed-length vector for n number of times. For example, a repeat vector transforms a fixed length vector having two-dimensional matrix of outputs into a three-dimensional matrix of outputs. In an encoder-decoder long short-term memory model the repeat vector can be incorporated as an adapter to establish a connection between the encoder and the decoder by transforming the fixed-length two-dimensional output of the encoder into varying-length three-dimensional input required by the decoder.

In this illustrative example, repeat vector606receives the fixed-length vector having a two-dimensional matrix of outputs from long short-term memory layer604and transforms the fixed-length vector into a three-dimensional matrix of outputs as required by long short-term memory layer608. In other words, the internal representation of the input sequence is repeated multiple times, once for each time step in the output sequence to form a sequence of vectors that are sent from repeat vector606to long short-term memory layer608.

Long short-term memory layer608can comprise a set of long short-term memory layers and a set of neurons. For example, long short-term memory layer608can comprise a layer comprising 200 long short-term memory neurons. In this example, long short-term memory layer608is a decoder in encoder-decoder long short-term memory model600. A decoder in a long short-term memory model transforms the learned internal representation of the input sequency by the encoder into the correct output sequence. In other words, the decoder transforms the fixed length vector from the encoder that has been converted into a three-dimensional matrix of outputs by the repeat vector into the correct output sequence.

The correct output sequence can be referred to as a complete output sequence having an expected length and dimension. For example, the correct output sequence can be generated using a connected layer to interpret each time step in the output sequence before the final output layer. In this example, the correct output sequence assists the long short-term memory model in making predictions.

In this illustrative example, long short-term memory layer608receives the three-dimensional matrix of outputs from repeat vector606and outputs the correct output sequence to time distributed layer610. Time distributed layers are utilized to apply one or more layers to a set of inputs to generate an output for each input in the set of inputs to pass the output to a subsequent layer in the neural network.

Time distributed layer610can comprise one or more neurons. For example, time distributed layer610can have 100 neurons.

In this example, time distributed layer610wraps an interpretation layer and output layer612of encoder-decoder long short-term memory model600. In other words, time distributed layer610applies layers to be used for each time step from the decoder. In this example, time distributed layer610is a time distributed wrapper that allows wrapped layers to be used in each time step from the decoder.

Wrapping the interpretation layer and output layer enables the decoder to determine the context required for each step in the output sequence and the wrapped dense layers to interpret each time step separately while using the same weights to perform the interpretation. As a result, time distributed layer610enables output layer612to predict a single step in the output sequence.

In this example, output layer612can have a number of time distributed outputs. The time distributed outputs are generated by hidden layers506. For example, outputs in output layer include predictions of future sequential maximum pressure levels that will occur for the aircraft on the portion of the flight path that the aircraft will flow based on the recorded sequential maximum pressure levels for the portion of the flight path already flown by the aircraft.

FIG.7is an illustration of a block diagram of a convolutional neural network long short-term memory encoder-decoder model is depicted in accordance with an illustrative embodiment. In this depicted example, convolutional neural network long short-term memory encoder-decoder model700is an example of one implementation for deep learning model500inFIG.5.

In this illustrative example, convolutional neural network long short-term memory encoder-decoder model700has a number of different components. As depicted in this example, convolutional neural network long short-term memory encoder-decoder model700comprises input layer702, 1D convolutional layer704, 1D convolutional layer706, 1D max pooling layer708, flatten layer710, repeat vector712, long short-term memory layer714, time distributed layer716, and output layer718. In this example, 1D convolutional layer704, 1D convolutional layer706, 1D max pooling layer708, flatten layer710, repeat vector712, long short-term memory layer714, and time distributed layer716in convolutional neural network long short-term memory encoder-decoder model700are examples of hidden layers506inFIG.5.

In this depicted example, input layer702has a number of inputs that can receive values for recorded sequential maximum pressure levels that have been recorded for a first portion of a flight path that an aircraft has flown so far. These values are used in the hidden layers to predict future sequential maximum pressure levels that will occur for the aircraft based on the recorded sequential maximum pressure levels input into input layer702. In this example, the future sequential maximum pressure levels are a second set of sequential maximum pressure levels that will be recorded as the aircraft flies a second portion of the flight path during the same flight.

The recorded sequential maximum pressure levels received at number of inputs in input layer702are sent to 1D convolutional layer704. This layer can receive a three-dimensional tensor and outputs a three-dimensional tensor. In this example, 1D convolutional layer704can also be referred to as temporal convolution. This layer creates a convolution kernel that is convolved with the layer input over a single spatial dimension to produce a tensor of outputs.

In this example, 1D convolutional layer704operates as an encoder. 1D convolutional layer704reads across the input sequence and projects the results onto feature maps. These feature maps can capture the result of applying filters to an input of a convolutional layer.

For example, 64 feature maps can be present at each convolutional layer. In this illustrative example, 1D convolutional layer704sends features maps to 1D convolutional layer706.

As depicted, 1D convolutional layer706reads across the input sequence of the feature maps received from 1D convolutional layer704and projects the results onto feature maps. In this example, 1D convolutional layer706sends the feature maps to 1D max pooling layer708.

As depicted, when 1D max pooling layer708receives a feature map, 1D max pooling layer708selects the maximum element from the region of the feature map covered by the filter. In other words, the output from 1D max pooling layer708is a feature map having a reduced dimension and containing the most prominent features of the previous feature map. In other words, 1D max pooling layer708can identify the presence of features, which include features that that have the most activated presence to form prominent features. For example, the brightest pixels can be prominent features.

In this illustrative example, 1D max pooling layer708outputs the most prominent features of the feature maps received from 1D convolutional layer706to flatten layer710.

As depicted, flatten layer710can receive multi-dimensional input tensors and flatten these tensors into a single dimension. Flatten layer710sends the flattened tensors to repeat vector712. In other words, flatten layer710can convert muti-dimensional arrays from pooled feature maps into a single fixed length linear vector to model the input layer and build the neural network model. In this example, flatten layer710transforms feature maps from 1D convolutional layer706to a fixed length linear vector.

In this example, the fixed-length vector represents the learned internal representation of the input sequence by 1D convolutional layer704and 1D convolutional layer706, which is the encoder stage in this example. Further, in this illustrative example, flatten layer710sends the fixed-length vector to repeat vector712.

As depicted, repeat vector712transforms a fixed-length vector by repeating the fixed-length vector for n number of times. For example, a repeat vector can transform a fixed length vector having single-dimensional matrix of outputs into a three-dimensional matrix of outputs. In convolutional neural network long short-term memory encoder-decoder model700, repeat vector712can be incorporated as an adapter to establish a connection between the encoder and the decoder by transforming the fixed-length single dimension output of the encoder into varying-length three-dimensional input required by the decoder. In other words, repeat vector712transforms the fixed-length vector from flatten layer710into a multi-dimensional matrix for input into long short-term memory layer714.

In this illustrative example, repeat vector712transforms the fixed-length vector received from flatten layer710into a three-dimensional matrix of outputs as required by long short-term memory layer714. In other words, the internal representation of the input sequence is repeated multiple times, once for each time step in the output sequence to form a sequence of vectors that are sent from repeat vector712to long short-term memory layer714.

Long short-term memory layer714can comprise a set of long short-term memory layers and a set of neurons. For example, long short-term memory layer714can comprise a layer comprising 200 long short-term memory neurons.

In this depicted example, long short-term memory layer714operates as a decoder of convolutional neural network long short-term memory encoder-decoder model700. A decoder in convolutional neural network long short-term memory encoder-decoder model700transforms the learned internal representation of the input sequency by the encoder into the correct output sequence. In other words, the decoder transforms the fixed length vector from the encoder that has been converted into a three-dimensional matrix of outputs by the repeat vector into the correct output sequence.

The correct output sequence can be referred to as a complete output sequence having an expected length and dimension. For example, the correct output sequence can be generated using a connected layer to interpret each time step in the output sequence before the final output layer. In this example, the correct output sequence assists the convolutional neural network long short-term memory encoder-decoder model in making predictions.

In this illustrative example, long short-term memory layer714receives the three-dimensional matrix of outputs from repeat vector712and outputs the correct output sequence to time distributed layer716. Time distributed layer716can comprise one or more neurons. For example, time distributed layer716can comprise 100 neurons.

In this depicted example, time distributed layer716wraps an interpretation layer and output layer718of convolutional neural network long short-term memory encoder-decoder model700. In other words, time distributed layer716applies layers to be used for each time step from the decoder. In this illustrative example, time distributed layer716is a time distributed wrapper that allows wrapped layers to be used in each time step from the decoder.

Wrapping the interpretation layer and output layer718enables the decoder to determine the context required for each step in the output sequence and the wrapped dense layers to interpret each time step separately while using the same weights to perform the interpretation. As a result, time distributed layer716enables output layer718to predict a single step in the output sequence.

In this example, output layer718can have a number of time distributed outputs. The time distributed outputs are generated by hidden layers. For example, outputs in output layer include predictions of future sequential maximum pressure levels that will occur for the aircraft on the portion of the flight path that the aircraft will flow based on the recorded sequential maximum pressure levels for the portion of the flight path already flown by the aircraft.

With reference next toFIG.8, an illustration of a block diagram of a convolutional long short-term memory encoder-decoder model is depicted in accordance with an illustrative embodiment. In this example, convolutional long short-term memory encoder-decoder model800is an example of one implementation for deep learning model500inFIG.5.

In this illustrative example, convolutional long short-term memory encoder-decoder model800has a number of different components. As depicted, convolutional long short-term memory encoder-decoder model800comprises input layer802, 1D convolutional long short-term memory layer804, flatten layer806, repeat vector808, long short-term memory layer810, time distributed layer812, and output layer814. In this example, 1D convolutional long short-term memory layer804, flatten layer806, repeat vector808, long short-term memory layer810, and time distributed layer812in convolutional long short-term memory encoder-decoder model800are examples of hidden layers506inFIG.5.

In this illustrative example, long short-term memory encoder-decoder model800uses convolutions directly as part of reading input into the long short-term memory neurons themselves. In other words, convolutional long short-term memory encoder-decoder model800is different from encoder-decoder long short-term memory model600that reads the data directly to calculate internal state and state transitions and is different from convolutional neural network long short-term memory encoder-decoder model700that interprets the output from convolutional neural network models.

In this depicted example, input layer802has a number of inputs that can receive values for recorded sequential maximum pressure levels that have been recorded for a first portion of a flight path that an aircraft has flown so far. These values received by inputs in input layer802are used sent to subsequent layers to predict future sequential maximum pressure levels that will occur for the aircraft based on the recorded sequential maximum pressure levels into input layer802. In this example, the future sequential maximum pressure levels are a second set of sequential maximum pressure levels that will be recorded as the aircraft flies a second portion of the flight path during the same flight.

The recorded sequential maximum pressure levels received at number of inputs in input layer802are sent to 1D convolutional long short-term memory layer804in convolutional long short-term memory encoder-decoder model800. A convolutional long short-term memory layer comprises both convolutional structures in both the input-to-state and state-to-state transitions and long short-term memory model structures to make spatial-temporal predictions. The convolutional long short-term memory layer uses convolutions directly as part of reading input into the long short-term memory neurons themselves. The convolutional long short-term memory layer determines the future state of a certain cell in the grid by the inputs and past states of its local neighbors.

In this example, 1D convolutional long short-term memory layer804acts as an encoder in convolutional long short-term memory encoder-decoder model800. 1D convolutional long short-term memory layer804reads across the input sequence and projects the results onto feature maps. These feature maps capture the result of applying filters to an input of a convolutional layer.

For example, 64 feature maps can be present at each convolutional layer. In this illustrative example, 1D convolutional long short-term memory layer804sends feature maps to flatten layer806.

As depicted flatten layer806is a layer in convolutional long short-term memory encoder-decoder model800. In this example, flatten layer806transforms feature maps from 1D convolutional long short-term memory layer804to a fixed length linear vector. In this example, the fixed-length vector represents the learned internal representation of the input sequence by 1D convolutional long short-term memory layer804, which is an encoder stage. Further, in this illustrative example, the fixed-length vector is an output from flatten layer806and is received by repeat vector808.

As depicted, repeat vector808is a layer in convolutional long short-term memory encoder-decoder model800. In convolutional long short-term memory encoder-decoder model800, repeat vector808can be incorporated as an adapter to establish a connection between the encoder and the decoder by transforming the fixed-length single dimension output of the encoder into varying-length three-dimensional input required by the decoder. In other words, repeat vector808transforms the fixed-length vector from flatten layer806into a multi-dimensional matrix for input into long short-term memory layer810.

In this illustrative example, repeat vector808transforms the fixed-length vector received from flatten layer806into a three-dimensional matrix of outputs as required by long short-term memory layer810. In other words, the internal representation of the input sequence is repeated multiple times, once for each time step in the output sequence to form a sequence of vectors that are sent from repeat vector808to long short-term memory layer810.

Long short-term memory layer810can comprise a set of long short-term memory layers and a set of neurons. For example, long short-term memory layer810can comprise a layer comprising 200 long short-term memory neurons.

In this depicted example, long short-term memory layer810operates as a decoder of convolutional long short-term memory encoder-decoder model800. In this illustrative example, long short-term memory layer810receives the three-dimensional matrix of outputs from repeat vector808and outputs the correct output sequence to time distributed layer812. In this depicted example, time distributed layer812can comprise one or more neurons. For example, time distributed layer812can comprise 100 neurons.

In this depicted example, time distributed layer812wraps an interpretation layer and output layer814of convolutional long short-term memory encoder-decoder model800. In other words, time distributed layer812applies layers to be used for each time step from the decoder. In this illustrative example, time distributed layer812is a time distributed wrapper that allows wrapped layers to be used in each time step from the decoder.

Wrapping the interpretation layer and output layer814enables the decoder to determine the context required for each step in the output sequence and the wrapped dense layers to interpret each time step separately while using the same weights to perform the interpretation. As a result, time distributed layer812enables output layer814to predict a single step in the output sequence.

In this example, output layer814can have a number of time distributed outputs. The time distributed outputs are generated by hidden layers506. For example, outputs in output layer include predictions of future sequential maximum pressure levels that will occur for the aircraft on the portion of the flight path that the aircraft will flow based on the recorded sequential maximum pressure levels for the portion of the flight path already flown by the aircraft.

The architectures of encoder-decoder long short-term model600, convolutional neural network long short-term memory encoder-decoder model700, or a convolutional long short-term memory encoder-decoder model800can be implemented using Keras. Keras is an open source software library that provides a Python interface for neural networks. Kera is a neural network library and provides a deep learning API written Python that runs on top of a machine learning platform TensorFlow.

Further, the illustrations of the different deep learning models inFIGS.6-8are not meant to limit the manner in which other illustrative examples can be implemented. Other deep learning architectures can be used that can provide predictions of future sequences of maximum sound pressure levels along the flight path from prior sequences of maximum sound pressure levels recorded for the flight path can be used.

Turning now toFIG.9, an illustration of a graph of a noise event is depicted in accordance with an illustrative embodiment. In this illustrative example, graph900is a graph of a noise event recorded by a microphone and a microphone system at an airport. As depicted, y-axis902is the sound level in dB and x-axis904is time in seconds.

As depicted, the noise level starts at ambient or background noise level at time equal to one second and level rises to a maximum level, representing the maximum sound pressure level (LAmax) as aircraft flies closer to the microphone. In this example, the maximum sound pressure level is of 76 dB at seven seconds. The noise level decreases as the aircraft proceeds into the distance. In this example, the noise event duration is 12 seconds.

In the illustrative examples, sequential maximum sound pressure levels, such as the one shown in graph900, can be predicted in real time for an unflown portion of a flight path can be predicted in real time in response to detecting sequential maximum sound pressure levels recorded for the portion of the flight path already flown.

Turning next toFIG.10, an illustration of a flight path for departures of an aircraft is depicted in accordance with an illustrative embodiment. In this illustrative example, aircraft1000flies on a flight path1001identified by waypoints1002. In this depicted example, waypoints1002comprises waypoints1-6. As depicted, waypoints1004are over location1003. Location1003includes airport1005and adjacent area1007.

As depicted, aircraft1000is located at waypoint3in waypoints1002. In this example, microphone12has minimum slant distance1009to aircraft1000. In this example, waypoint3can be location for the time at which microphone12records the maximum sound pressure level for aircraft1000.

Waypoint6defines the portion of the flight in flight path1001that has been flown by aircraft1000. Aircraft1000has already flown on the portion of flight path1001with waypoint1and waypoint2. Microphone1has recorded the maximum sound pressure level for aircraft1000at waypoint1. Maximum sound pressure level for aircraft1000has also been recorded by microphone14at waypoint2. Further, maximum sound pressure level for aircraft1000has also been recorded by microphone12at waypoint3.

These three recorded maximum sound pressure levels are a first set of sequential maximum sound pressure levels recorded by a first consecutive set of microphones, microphone1, microphone12, and microphone14.

A second set of sequential maximum sound pressure levels that will be recorded by a second consecutive set of microphones can be predicted using deep learning models in these examples. The second consecutive set of microphones are microphone10, microphone7, and microphone5for when aircraft1000flies to waypoint4, waypoint5, and waypoint6, respectively, in flight path1001that is yet to be flown.

Thus, this prediction is performed using the maximum sound pressure levels recorded at microphone1at waypoint1, microphone14at waypoint2, and microphone12at waypoint3. The sound pressure levels and other information about the sound pressure can be received as sound data and can be part of airport data received from airport.

The prediction is for maximum sound pressure levels that will be recorded by microphones at the next three waypoints, microphone10at waypoint4, microphone7at waypoint5, and microphone5at waypoint6. This prediction is performed in real time as aircraft1000flies on flight path1001.

In other illustrative examples, other numbers of recorded maximum number of sound pressure levels can be used to predict other numbers of maximum sound pressure levels. For example, the first set of sequential maximum sound pressure levels can be 3 while the second set of sequential maximum sound pressure levels can be 4. In another example, the first set of sequential maximum sound pressure levels can be 2 while the second set of sequential maximum sound pressure levels can be 5. These and other numbers of inputs and outputs can be used depending on the particular implementation.

Further, in this example, sensor data from sensor readings for aircraft1000recorded at the different way points can be correlated with other parameters to create training data in which the maximum sound pressure levels is correlated with selected features. In other words, the maximum sound pressure level can be used as a label for: sensor data, atmospheric data, and airport data, for the time instance at which microphone12records sound for aircraft1000at waypoint3if supervised learning is used with the deep learning models.

With reference now toFIG.11, an illustration of graphs of sensor data for selected parameters are depicted in accordance with an illustrative embodiment. In this illustrative example, graphs1100are graphs for parameters such as Mach number1102, altitude (AltGeo)1104, N1C1106, pitch1108, spoiler1110, flap21112, weight1114, atmospheric absorption (AtmAbsorp)1116, fuel flow (FuelFlow)1118, and minimum slant distance (MinSlantDist)1120. The y-axes for these graphs indicate values for the associated parameters. For example, atmospheric absorption (AtmAbsorp)1116can be expressed in dBA. As another example, minimum slant distance (MinSlantDist)1120can be in mm. The X axes of graphs1100show time.

With the maximum sound pressure level LAmax being recorded by each microphone for an entire arrival or departure, the process can identify which time instance in graphs1100should be correlated to the ground truth for maximum sound pressure level LAmax to train deep learning models. In identifying the time instance, the process determines which time instance should be selected.

Heuristics searching can be performed to identify the best matching time instance. In the illustrative example, the best matching time instance is a time instance in which the slant distance to the microphone is below a threshold and selected parameters remain relatively stable with no radical changes. In this example, parameters that are relatively stable can be referred to as stable parameters. Stable parameters can be present when a moving average of the selected parameter in graph1100are within a threshold.

In this illustrative example, time window1122is used as part of a search to identify a best matching time instance for the selected parameters in graphs1100.

These different parameters are examples of parameters in other illustrative examples, the selection of the particular time instance can involve analyzing other numbers of parameters. N1C1106in graph100is representative of engine thrust. N1C indicates the rotational speed of a low pressure (low speed) spool in an engine, usually calibrated in percent of an engine manufacturer defined maximum rotational speed that corresponds to 100%. Without limitation, graphs1100can include 50 selected parameters, 200 selected parameters, 750 selected parameters or other smaller or larger number of parameters in other illustrative examples.

In this example, a heuristic search algorithm using Auto Regressive Integrated Moving Average (ARIMA)—like sliding window approach can be used. With this example the average value for each parameter is compared with the value of that parameter at each time instance included in the window to determine whether parameters are stable. With this process, the search can begin from the time instance where the slant distance between the microphone and aircraft is a minimum and that search expands on both sides of this time instance where the slant distance between the microphone and aircraft is a minimum. The search ends as soon as the slant distance is the minimum or in the neighborhood of the minimum where all the selected parameters are stable. For example, the selected parameters can be considered to be stable when the values of the selected parameters do not exceed the moving average.

With reference toFIG.12, an illustration of a flowchart of a process for predicting sequential maximum sound pressure levels generated by an aircraft is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.12can be implemented using computer system212and controller214inFIG.2. For example, the process can be implemented in controller214in computer system212inFIG.2.

As depicted, the process begins by training a set of deep learning models to predict the sequential maximum sound pressure levels generated by the aircraft for a flight path over a location using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by microphones in a microphone system for flight paths over the location (operation1200). The training in operation1200can be performed using unsupervised training.

The process identifies a first set of the sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along the flight path during a flight of the aircraft using the flight path (operation1202). The process predicts a second set of the sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of the sequential maximum sound pressure levels is predicted using the set of deep learning models after training the set of deep learning models using the training dataset (operation1204). The process terminates thereafter.

In this example inFIG.12, the first number of the first consecutive set of the microphones can be different from the second number of the second consecutive set of the microphones. In other words, the number of microphones used in each consecutive set of microphones do not have to be the same.

With reference toFIG.13, an illustration of a flowchart of a process adjusting weights is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.13is an example of an operation that can be performed with the operations inFIG.12.

The process adjusts a set of weights in the set of deep learning models using errors employing backward propagation to reduce error between the actual output of a layer in a deep learning model from forward propagation and a desired output for the layer in the deep learning model (operation1300). The process terminates thereafter. In this example, each selected parameter in the selected parameters is assigned a different weight from other selected parameters in a deep learning model in the set of deep learning models. The assignment is made as part of initializing the weights for training the set of deep learning models.

Turning next toFIG.14, an illustration of a flowchart of a process for predicting sequential maximum sound pressure levels in a cloud environment is depicted in accordance with an illustrative embodiment. The operations inFIG.14can be performed with the operations inFIG.12. In this example, controller214can be implemented in a microservice in a cloud computing environment. A microservice is software in the form of a self-contained piece of functionality that communicates using a known interface such as application programming interface (API). Microservices can be used to build a distributed application using containers. Each function in the application operates as an independent service that can be updated without disrupting other services in an application.

The process begins by receiving, by a microservice in the cloud computing environment, a request to predict the sequential maximum sound pressure levels that will be generated by the aircraft for the flight path over the location using the set of deep learning models as the aircraft flies using the flight path (operation1400). The request in operation1400can include a first set of the sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path. This request is received in real time as the aircraft flies using the flight path. The request can also include other input data such as aircraft sensor data and atmospheric data in addition to the sound data with the first set of the sequential maximum sound pressure levels. In this example, the prediction can be made using operation1204inFIG.12.

The process returns, by the microservice in the cloud computing environment, a response to the request to predict the sequential maximum sound pressure levels that will be generated by the aircraft for the flight path over the location using the set of deep learning models as the aircraft flies using the flight path (operation1402). The process terminates thereafter. In operation1402, the prediction made in operation1204inFIG.12is returned in the response.

The response is a prediction that includes a second set of the sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location. This second set of the sequential maximum sound pressure levels are sequential maximum sound pressure levels that have not yet been recorded at the time of the request in operation1400. The deep learning models are used to generate a response that can be used to take one or more actions before the aircraft generates the second set of the sequential maximum sound pressure levels.

The speed at which the prediction is made is fast enough to allow a corrective action to be taken with respect to the aircraft in case the second set of the sequential maximum sound pressure levels will be greater than a threshold for sequential maximum sound pressure levels. This speed of prediction is faster than can be performed by human operators. The real time prediction allows for a practical application of the prediction to reduce noise.

With reference now toFIG.15, an illustration of a flowchart of a process for performing feature engineering on historical sensor data is depicted in accordance with an illustrative embodiment. The operations in this figure are examples of an additional operation that can be used within the operations in the process inFIG.12.

The process performs feature engineering on: the historical aircraft sensor data for the selected parameters, the historical atmospheric data, and the historical sound data recorded by the microphone system. that may include at least one of: selecting relevant features, handling missing data, computing additional parameters, normalizing data, standardizing the data, or performing dimensionality reduction (operation1500). The process terminates thereafter.

With reference now toFIG.16, an illustration a flowchart of a process for selecting parameters is depicted in accordance with an illustrative embodiment. The operations in this figure are examples of additional operations that can be used within the operations in the process inFIG.12.

The process selects parameters that have a correlation to the maximum sound pressure level to form correlated parameters (operation1600). The process removes the correlated parameters that are repetitive to form the selected parameters (operation1602). The process terminates thereafter.

With reference now toFIG.17, an illustration a flowchart of a process for creating a training dataset from sensor data is depicted in accordance with an illustrative embodiment. The operations in this figure are examples of an operation that can be used within the operations in the process inFIG.12.

The process creates the training dataset from the historical aircraft sensor data for the selected parameters, the historical atmospheric data, and the historical sound data recorded by the microphone system for the flight paths over the location (operation1700). The process terminates thereafter.

In this example, the training dataset comprises groups of data. Each group of data in the groups of data is for a flight in the flights and includes the maximum sound pressure levels detected by microphones in the microphone system and the selected parameters corresponding to the time instances for the maximum sound pressure levels for the flight. The maximum sound pressure levels can be used as labels for the selected parameters and the historical atmospheric data.

With reference now toFIG.18, an illustration a flowchart of a process for selecting a time instance is depicted in accordance with an illustrative embodiment. The process inFIG.18is an example of one implementation for operation1700inFIG.17.

The process selects a time instance where a smallest slant distance is present between the aircraft and a microphone in which all of the selected parameters for the time instance are stable parameters (operation1800). The process terminates thereafter.

With reference now toFIG.19, an illustration a flowchart of a process for selecting additional time instances is depicted in accordance with an illustrative embodiment. The operations in this figure are examples of additional operations that can be used within the operations in the process inFIG.18.

The process repeats, for each microphone in the microphone system that recorded the historical sound data, selecting the time instance where the smallest slant distance is preset between the aircraft and the microphone in which all of the selected parameters for the time instance are stable parameters (operation1900). The process terminates thereafter.

With reference now toFIG.20, an illustration of a flowchart of a process for identifying a deep learning model is depicted in accordance with an illustrative embodiment. The operations in this figure are examples of additional operations that can be used within the operations in the process inFIG.12.

The process identifies a deep learning model from the different deep learning models having a highest level of accuracy in predicting maximum sound pressure levels, wherein a deep learning model having the highest level of accuracy is a selected deep learning model for use in predicting the maximum sound pressure level (operation2000). The process predicts the maximum sound pressure level for the flight path of the aircraft over the location using the selected deep learning model (operation2002). Operation2002is an example of an implementation of operation1204inFIG.12. The process terminates thereafter.

With reference now toFIG.21, an illustration of a flowchart of a process for continuing to train different deep learning models is depicted in accordance with an illustrative embodiment. The operations in this figure are examples of additional operations that can be used within the operations in the process inFIG.20.

The process continues to train the different deep learning models using new training datasets generated from new aircraft sensor data for the selected parameters, new atmospheric data, and new sound data recorded by the microphone system (operation2100). The process repeats identifying the deep learning model from the different deep learning models having the highest level of accuracy in predicting the maximum sound pressure levels in response to continuing to train the different deep learning models (operation2102). The process terminates thereafter.

With reference toFIG.22, an illustration of a flowchart of a process for predicting sequential maximum sound pressure levels generated by an aircraft is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.22can be implemented using computer system212and controller214inFIG.2. For example, the process can be implemented in controller214in computer system212inFIG.2.

The process begins by identifying a first set of sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along a flight path during a flight of the aircraft using the flight path (operation2200). The process predicts a second set of sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of sequential maximum sound pressure levels is predicted using the set of deep learning models after training the set of deep learning models using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by microphones in a microphone system for flight paths over the location (operation2202).

The process performs a set of actions using a prediction of the second set of the sequential maximum sound pressure levels generated by the aircraft for the flight path over the location made by the set of deep learning models (operation2204). The process terminates thereafter. In operation2204, wherein the set of actions is selected from at least one of planning a future flight path over the location using the prediction of the second set of the sequential maximum sound pressure levels generated by the aircraft for the flight path over the location, determining compliance with a regulation regarding the maximum sound pressure levels for the location, changing a future portion of the flight path, or changing an aircraft configuration for the aircraft for the future portion of the flight path.

With reference toFIG.23, an illustration of a flowchart of a process for identifying time instances for training data is depicted in accordance with an illustrative embodiment. The process illustrated inFIG.23is an example of one implementation for operation1800inFIG.18.

The process begins by selecting a time instance with a smallest slant distance to form a selected time instance (operation2300). The process defines a window around the selected time instance (operation2302). In operation2302, selected time instance is in the center of the window or as close to the center as possible. The size of the time window can be a default or preselected size such as 3, 5, or 7 time instances wide. Although odd numbered time instances are shown, other examples can use windows with even numbered instances.

The process then determines whether all of the time instances within the window have stable parameters (operation2304). If all of the time instances within window have stable parameters, the process uses the selected time instance for correlation with aircraft sensor data for selected parameters, historical atmospheric data, and sound data for using in creating training data (operation2306). The process terminates thereafter. The selected time instance in operation2306is for a single flight. In operation2306, this data can be associated with a label that is the maximum pressure level value.

With reference again to operation2304, if the smallest slant distance where stable parameters are present is not less than the threshold, the process shifts the window (operation2308) the process then returns to operation2304. The window can be shifted in either direction. In operation2308, the window can also be changed in size.

The flowcharts and block diagrams in the different depicted embodiments illustrate the architecture, functionality, and operation of some possible implementations of apparatuses and methods in an illustrative embodiment. In this regard, each block in the flowcharts or block diagrams can represent at least one of a module, a segment, a function, or a portion of an operation or step. For example, one or more of the blocks can be implemented as program code, hardware, or a combination of the program code and hardware. When implemented in hardware, the hardware can, for example, take the form of integrated circuits that are manufactured or configured to perform one or more operations in the flowcharts or block diagrams. When implemented as a combination of program code and hardware, the implementation may take the form of firmware. Each block in the flowcharts or the block diagrams can be implemented using special purpose hardware systems that perform the different operations or combinations of special purpose hardware and program code run by the special purpose hardware.

In some alternative implementations of an illustrative embodiment, the function or functions noted in the blocks may occur out of the order noted in the figures. For example, in some cases, two blocks shown in succession may be performed substantially concurrently, or the blocks may sometimes be performed in the reverse order, depending upon the functionality involved. Also, other blocks may be added in addition to the illustrated blocks in a flowchart or block diagram.

Turning now toFIG.24, an illustration of a block diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system2400can be used to implement server computer104, server computer106, client devices110, inFIG.1. Data processing system2400can also be used to implement computer system212inFIG.2. In this illustrative example, data processing system2400includes communications framework2402, which provides communications between processor unit2404, memory2406, persistent storage2408, communications unit2410, input/output (I/O) unit2412, and display2414. In this example, communications framework2402takes the form of a bus system.

Processor unit2404serves to execute instructions for software that can be loaded into memory2406. Processor unit2404includes one or more processors. For example, processor unit2404can be selected from at least one of a multicore processor, a central processing unit (CPU), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a network processor, or some other suitable type of processor. Further, processor unit2404can may be implemented using one or more heterogeneous processor systems in which a main processor is present with secondary processors on a single chip. As another illustrative example, processor unit2404can be a symmetric multi-processor system containing multiple processors of the same type on a single chip.

Memory2406and persistent storage2408are examples of storage devices2416. A storage device is any piece of hardware that is capable of storing information, such as, for example, without limitation, at least one of data, program code in functional form, or other suitable information either on a temporary basis, a permanent basis, or both on a temporary basis and a permanent basis. Storage devices2416may also be referred to as computer-readable storage devices in these illustrative examples. Memory2406, in these examples, can be, for example, a random-access memory or any other suitable volatile or non-volatile storage device. Persistent storage2408can take various forms, depending on the particular implementation.

For example, persistent storage2408may contain one or more components or devices. For example, persistent storage2408can be a hard drive, a solid-state drive (SSD), a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination of the above. The media used by persistent storage2408also can be removable. For example, a removable hard drive can be used for persistent storage2408.

Communications unit2410, in these illustrative examples, provides for communications with other data processing systems or devices. In these illustrative examples, communications unit2410is a network interface card.

Input/output unit2412allows for input and output of data with other devices that can be connected to data processing system2400. For example, input/output unit2412can provide a connection for user input through at least one of a keyboard, a mouse, or some other suitable input device. Further, input/output unit2412can send output to a printer. Display2414provides a mechanism to display information to a user.

Instructions for at least one of the operating system, applications, or programs can be located in storage devices2416, which are in communication with processor unit2404through communications framework2402. The processes of the different embodiments can be performed by processor unit2404using computer-implemented instructions, which can be located in a memory, such as memory2406.

These instructions are program instructions and are also referred to as program code, computer usable program code, or computer-readable program code that can be read and executed by a processor in processor unit2404. The program instructions in the different embodiments can be embodied on different physical or computer-readable storage media, such as memory2406or persistent storage2408.

Program instructions2418are located in a functional form on computer-readable media2420that is selectively removable and can be loaded onto or transferred to data processing system2400for execution by processor unit2404. Program instructions2418and computer-readable media2420form computer program product2422in these illustrative examples. In the illustrative example, computer-readable media2420is computer-readable storage media2424.

Computer-readable storage media2424is a physical or tangible storage device used to store program instructions2418rather than a media that propagates or transmits program instructions2418. Computer readable storage media2424, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Alternatively, program instructions2418can be transferred to data processing system2400using a computer-readable signal media. The computer-readable signal media are signals and can be, for example, a propagated data signal containing program instructions2418. For example, the computer-readable signal media can be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals can be transmitted over connections, such as wireless connections, optical fiber cable, coaxial cable, a wire, or any other suitable type of connection.

Further, as used herein, “computer-readable media2420” can be singular or plural. For example, program instructions2418can be located in computer-readable media2420in the form of a single storage device or system. In another example, program instructions2418can be located in computer-readable media2420that is distributed in multiple data processing systems. In other words, some instructions in program instructions2418can be located in one data processing system while other instructions in program instructions2418can be located in one data processing system. For example, a portion of program instructions2418can be located in computer-readable media2420in a server computer while another portion of program instructions2418can be located in computer-readable media2420located in a set of client computers.

The different components illustrated for data processing system2400are not meant to provide architectural limitations to the manner in which different embodiments can be implemented. In some illustrative examples, one or more of the components may be incorporated in or otherwise form a portion of, another component. For example, memory2406, or portions thereof, can be incorporated in processor unit2404in some illustrative examples. The different illustrative embodiments can be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system2400. Other components shown inFIG.24can be varied from the illustrative examples shown. The different embodiments can be implemented using any hardware device or system capable of running program instructions2418.

Some features of the illustrative examples are described in the following clauses. These clauses are examples of features not intended to limit other illustrative examples.

Clause 1

A method for predicting sequential maximum sound pressure levels generated by an aircraft, the method comprising:training a set of deep learning models to predict the sequential maximum sound pressure levels generated by the aircraft for a flight path over a location using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by microphones in a microphone system for flight paths over the location;identifying a first set of the sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along the flight path during a flight of the aircraft using the flight path; andpredicting a second set of the sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of the sequential maximum sound pressure levels is predicted using the set of deep learning models after training the set of deep learning models using the training dataset.
Clause 2

The method according to clause 1 further comprising:adjusting a set of weights in the set of deep learning models using errors using backward propagation to reduce error between an actual output of a layer in a deep learning model from forward propagation and a desired output for the layer in the deep learning model.
Clause 3

The method according to clause 2, wherein each selected parameter in the selected parameters is assigned a different weight from other selected parameters in the deep learning model in the set of deep learning models

Clause 4

The method according to one of clauses 1, 2, or 3, wherein the set of deep learning models uses autoregression for time series forecasting, wherein the first set of the sequential maximum sound pressure levels recorded by the first consecutive set of the microphones is a function of prior sequential time steps input into the set of deep learning models and the set of deep learning models outputs the second set of the sequential maximum sound pressure levels as a function of subsequent sequential time steps.

Clause 5

The method according to one of clauses 1, 2, 3, or 4, wherein the first consecutive set of the microphones is different from the second consecutive set of the microphones.

Clause 6

The method according to one of clauses 1, 2, 3, 4, or 5, wherein the set of deep learning models is selected from at least one of an encoder-decoder long short-term memory model, a convolutional neural network long short-term memory encoder-decoder model, or a convolutional long short-term memory encoder-decoder model.

Clause 7

The method according to one of clauses 1, 2, 3, 4, 5, or 6, wherein the set of deep learning models are located in a cloud computing environment.

Clause 8

The method according to clause 7 further comprising:receiving, by a microservice in the cloud computing environment, a request to predict the sequential maximum sound pressure levels that will be generated by the aircraft for the flight path over the location using the set of deep learning models as the aircraft flies using the flight path.
Clause 9

The method according to one of clauses 1, 2, 3, 4, 5, 6, 7, or 8, wherein a deep learning model in the set of deep learning models comprises an input layer, a set of hidden layers, and an output layer, wherein the input layer receives a first number of time steps and the output layer outputs a second number of time steps.

Clause 10

The method according to one of clauses 1, 2, 3, 4, 5, 6, 7, 8, or 9, wherein the set of deep learning models comprises different deep learning models and further comprising:identifying a deep learning model from the different deep learning models having a highest level of accuracy in predicting maximum sound pressure levels, wherein the deep learning model having the highest level of accuracy is a selected deep learning model for use in predicting the sequential maximum sound pressure levels; andwherein predicting the second set of the sequential maximum sound pressure levels that will be recorded by the second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location comprises:predicting the second set of the sequential maximum sound pressure levels that will be recorded by the second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of the sequential maximum sound pressure levels is predicted using the selected deep learning model.
Clause 11

The method according to one of clauses 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, wherein the flight path is for one of a departing flight from an airport and an arriving flight to the airport.

Clause 12

A method for predicting sequential maximum sound pressure levels generated by an aircraft, the method comprising:identifying a first set of the sequential maximum sound pressure levels recorded by a first consecutive set of microphones along a flight path during a flight of the aircraft using the flight path; andpredicting a second set of the sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over a location, wherein the second set of the sequential maximum sound pressure levels is predicted using a set of deep learning models after training the set of deep learning models using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by the microphones in a microphone system for flight paths over the location.
Clause 13

The method according to clause 12 further comprising:performing a set of actions using a prediction of the second set of the sequential maximum sound pressure levels generated by the aircraft for the flight path over the location made by the set of deep learning models.
Clause 14

The method according to clause 13, wherein the set of actions is selected from at least one of planning a future flight path over the location using the prediction of the second set of the sequential maximum sound pressure levels generated by the aircraft for the flight path over the location, determining compliance with a regulation regarding maximum sound pressure levels for the location, changing a future portion of the flight path, or changing an aircraft configuration for the aircraft for the future portion of the flight path.

Clause 15

A deep learning system for sequential sound pressure level prediction comprising:a computer system; anda controller in the computer system, wherein controller is configured to:train a set of deep learning models to predict sequential maximum sound pressure levels generated by an aircraft for a flight path over a location using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by microphones in a microphone system for flight paths over the location;identify a first set of the sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along the flight path during a flight of the aircraft using the flight path; andpredict a second set of the sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of the sequential maximum sound pressure levels is predicted using the set of deep learning models after training the set of deep learning models using the training dataset.
Clause 16

The deep learning system according to clause 15, wherein the controller is configured to:adjust a set of weights in the set of deep learning models using errors using backward propagation to reduce error between an actual output of a layer in a deep learning model from forward propagation and a desired output for the layer in the deep learning model.
Clause 17

The deep learning system according to clause 16, wherein each selected parameter in the selected parameters is assigned a different weight from other selected parameters in the deep learning model in the set of deep learning models.

Clause 18

The deep learning system according to one of clauses 15, 16, or 17, wherein the set of deep learning models uses autoregression for time series forecasting, wherein a first number of the sequential maximum sound pressure levels recorded by the first number of the microphones is a function of prior sequential time steps input into the set of deep learning models and the set of deep learning models outputs the second set of the sequential maximum sound pressure levels as a function of subsequent sequential time steps.

Clause 19

The deep learning system according to one of clauses 15, 16, 17, or 18, wherein a first number of the first consecutive set of the microphones is different from a second number of the second consecutive set of the microphones.

Clause 20

The deep learning system according to one of clauses 15, 16, 17, 18, or 19, wherein the set of deep learning models is selected from at least one of an encoder-decoder long short-term memory model, a convolutional neural network long short-term memory encoder-decoder model, or a convolutional long short-term memory encoder-decoder model.

Clause 21

The deep learning system according to one of clauses 15, 16, 17, 18, 19, or 20, wherein the set of deep learning models are located in a cloud computing environment.

Clause 22

The deep learning system according to clause 21, wherein the controller is configured to:receive, by a microservice in the cloud computing environment, a request to predict the sequential maximum sound pressure levels generated by the aircraft for the flight path over the location using the set of deep learning models.
Clause 23

The deep learning system according to one of clauses 15, 16, 17, 18, 19, 20, 21, or 22, wherein a deep learning model in the set of deep learning models comprises an input layer, a set of hidden layers, and an output layer, wherein the input layer receives a first number of time steps and the output layer outputs a second number of the time steps.

Clause 24

The deep learning system according to one of clauses 15, 16, 17, 18, 19, 20, 21, 21, or 23, wherein the set of deep learning models comprises different deep learning models and, wherein the controller is configured to:identify a deep learning model from the different deep learning models having a highest level of accuracy in predicting maximum sound pressure levels, wherein the deep learning model having the highest level of accuracy is a selected deep learning model for use in predicting the sequential maximum sound pressure levels; andwherein in predicting the second set of the sequential maximum sound pressure levels that will be recorded by the second consecutive set of the microphones during the flight of the aircraft using the flight path over the location, the controller is configured to:predict the second set of the sequential maximum sound pressure levels that will be recorded by the second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of the sequential maximum sound pressure levels is predicted using the selected deep learning model.
Clause 25

The deep learning system according to one of clauses 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24, wherein the flight path is for one of a departing flight from an airport and an arriving flight to the airport.

Clause 26

A computer program product for predicting sequential maximum sound pressure levels, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer system to cause the computer system to perform a method of:training a set of deep learning models to predict the sequential maximum sound pressure levels generated by an aircraft for a flight path over a location using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by microphones in a microphone system for flight paths over the location;recording a first set of the sequential maximum sound pressure levels recorded by a first consecutive set of the microphones along the flight path during a flight of the aircraft using the flight path; andpredicting a second set of the sequential maximum sound pressure levels that will be recorded by a second consecutive set of the microphones along the flight path during the flight of the aircraft using the flight path over the location, wherein the second set of the sequential maximum sound pressure levels is predicted using the set of deep learning models after training the set of deep learning models using the training dataset.

Thus, illustrative examples provide a method, apparatus, system, and computer program product for predicting noise in the form of maximum sound pressure levels. The prediction of maximum sound pressure levels is performed using a deep learning model that has been trained using historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data reported by microphone system for flight paths over a location. In these illustrative examples, a prediction of a maximum sound pressure level can be made for a flight path over the location.

With these predictions of sequential sound pressure level has increased accuracy. Airlines can use these predictions to plan flight paths that reduce undesired noise in areas near an airport. Further, airports and regulators can more easily enforce noise-reduced arrival and departure procedures and penalizing airlines for their flights that produce noise above mandated thresholds for maximum sound pressure levels. With more accurate predictions using deep learning models, meeting thresholds for noise levels can occur more easily for airlines.

The description of the different illustrative embodiments has been presented for purposes of illustration and description and is not intended to be exhaustive or limited to the embodiments in the form disclosed. The different illustrative examples describe components that perform actions or operations. In an illustrative embodiment, a component can be configured to perform the action or operation described. For example, the component can have a configuration or design for a structure that provides the component an ability to perform the action or operation that is described in the illustrative examples as being performed by the component. Further, To the extent that terms “includes”, “including”, “has”, “contains”, and variants thereof are used herein, such terms are intended to be inclusive in a manner similar to the term “comprises” as an open transition word without precluding any additional or other elements.

Many modifications and variations will be apparent to those of ordinary skill in the art. Further, different illustrative embodiments may provide different features as compared to other desirable embodiments. The embodiment or embodiments selected are chosen and described in order to best explain the principles of the embodiments, the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.