Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm

An online experiment to acquire the interior noise of a China Railways High-speed （CRH） train showed that it wasmainly composed of middle-low frequency components and could not be described properly by linear or A-weighted soundpressure level （SPL）. Thus, the appropriate way to evaluate the high-speed train interior noise is to use sound quality parameters,and the most important is loudness. To overcome the disadvantages of the existing loudness algorithms, a novel signal-adaptiveMoore loudness algorithm （AMLA） based on the equivalent rectangular bandwidth （ERB） spectrum was introduced. The valida-tion reveals that AMLA can obtain higher accuracy and efficiency, and the simulated dark red noise conforms best to thehigh-speed train interior noise by loudness and auditory assessment. The main loudness component of the interior noise is below27.6 ERB rate （erbr）, and the sound quality of the interior noise is relatively stable between 300-350 km/h. The specific loudnesscomponents among 12-15 erbr stay invariable throughout the acceleration or deceleration process while components among20-27 erbr are evidently speed related. The unusual random noise is effectively identified, which indicates that AMLA is anappropriate method for sound quality assessment of the high-speed train under both steady and transient conditions.