Themodel of the sound environment with the use of controlled samples differs from theusual ones, including the fact that it can not use independent models for each noise,due to the complexity of isolating “clean” samples containing the soundsof only their sources of noise. The model of a particularnoise source, as a rule, depends on the models of other noise: ,Where -the model under consideration, -noise parameters, -parameters of the noise source, -a lot of models of other noises.This problem is solved by choosing the most independentsamples and taking into account the remaining dependencies when choosing the managementof their parameters. Bylistening and analyzing records, the developer finds areas where the noise of thissource is the least “littered” with other noises.
Thearea should be long enough. Next,it is necessary to correlate the selected noise with the parameters of its sourceoperation. This can be done using the indicator informationin the cabin, which has got into the video image of the recording, and in its absence- using spectral analysis and knowledge of operating modes of the aircraft’s mechanismsin different flight modes. For example, the noisespectrum of the screw usually has characteristic peaks representing the main rotationalspeed of the screw and its harmonics multiplied by the number of blades: ,Where -frequency of the i- th harmonic, -the frequency of the screw, -number of propeller blades, -Harmonic number.
Recalculating the frequenciesof the harmonics into the frequency of the screw and comparing it with the nominaloperating conditions of the power plant, it is possible to isolate the correspondingcharacteristic sections of the recording (see the example in Fig.2). Fig.
2.Analysis of noise spectrum of a turboprop aircraft The influence of models of individual noise on eachother can be taken into account on the basis of knowledge of the mutual dependenciesof the corresponding mechanisms in different flight regimes. Forexample, the noise pattern of a propeller of a turboprop aircraft in takeoff andlanding and cruising modes, as a rule, can not be effectively separated from turbinenoise. At the same time, a linear change in the loudnessand frequency bands of such a sample leads to a “failure” of the frequencycomponents of the turbine at low revolutions of the screw, which “by ear”is manifested as the absence of turbine noise. Therefore,a turbine sample must be extracted from another part of the record, for example,at the time when the screw starts spinning, when its noise is insignificant, andthe control dependence must take into account the noise amplification of the turbinein the noise pattern of the screw by reducing the loudness characteristic (Fig.3) Fig. 3.
Illustration of the volume correction of image playback to take into account theirinterdependence The problem of the lack of data on the absolute levelsof noise in the cabin can only be solved using expert judgment. First you need to get therecording of the sounds of the aircraft. The simplest method is to record the timeof flight.The resulting recording provides a panoramic viewof the sound at different stages of the flight and serves as part of the specimen.Part of the recording oscilloscope, from the stage of engine start up to the transitionto cruise flight mode, is shown in Fig. 5.
The main problem when working with suchrecords may be the lack of identification of sources of noise and their bindingto the parameters of the mechanisms. The subsequent identification of the sourcesin this case should be carried out expertly, by listening to the characteristicparts of the record and their reconciliation with the operational documentationof the aircraft. In this way, it will be possible to identify the main stages ofthe flight (Fig. 5) and identify the main sources of noise (starters, engines, screws).The binding of these noise to the parameters of their sources is carried out usingspectral analysis. To do this, in the spectrum was the most powerful and characteristiccomponent of the work of the aircraft (for example, the revolutions of the propeller).
Further, as images were selected fragments with the most pronounced noises of onesource and the smallest components of others. Fig.5. Oscillogramof the flight sound fragment of the aircraft.