For decades, the automotive and aerodynamics industries have used acoustical measurements to understand how sounds from turbines, motors, and other physical actions affect humans. Until recently, the analysis performed on those measurements has been quite simple, consisting of sound pressure level analysis, octave analysis, FFT analysis, and the application of basic weighting filters. These algorithms, while good at revealing the decibel level or frequency content of a signal, do not uncover a number of important phenomena that determine the desirability of the signal. To move beyond simple noise level analysis and perform practical environmental noise measurements, sound quality algorithms have been developed to explain how sounds are perceived by the human ear.
Sound quality algorithms are the product of research across a spectrum of sciences including acoustics, physics, communication engineering, mechanical engineering, musicology, marketing, physiology, and psychology. These algorithms combine the psychoacoustical, physical, and cognitive aspects of sound in order to provide new performance metrics to design engineers. An example application of these algorithms in the automotive NVH industry is to design an engine with a more pleasing sound or a door handle with a more soothing click. The sound quality algorithms also are applicable in the production of consumer electronics. These algorithms allow an engineer to design a better-sounding product, which psychologically increases the chances of consumer adoption.
Sound quality consists of the following algorithms: ISO 532B stationary loudness, time-varying loudness, Aures roughness, Aures sharpness, Aures tonality, and fluctuation strength.