Sound quality, a set of algorithms developed from the science of psychoacoustics, is used to define the relationship between the physical quantities of sound and the subjective impression as heard by the human ear. These algorithms examine physical parameters such as sound pressure level, frequency, and modulation depth and correlate them to human hearing perception. Sound quality algorithms have revolutionized acoustical measurements in the automotive, white goods, and other industries by transitioning the focus from simple noise emission reduction to a more sophisticated process of increasing sound desirability. Sound quality consists of the following algorithms: ISO 532B stationary loudness, time-varying loudness, Aures roughness, Aures sharpness, Aures tonality, and fluctuation strength. The LabVIEW Sound and Vibration Toolkit provides LabVIEW VIs with which you can implement these algorithms.
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.
Loudness is a term referring to the human perception of sound volume. The definition of loudness states that 1 sone, the unit of loudness, corresponds to a 1 kHz tone at 40dB. The loudness scale quantifies the loudness linearly to the human ear in which a doubling of the sone-value maps directly to a doubling in loudness.
Stationary loudness is an algorithm for steady-state noises, or signals that do not vary with time. This algorithm measures the 1/3 octave spectrum, combines the fractional-octave bands into critical bands, and then applies spectral masking. This algorithm returns the result as the specific loudness versus critical band rate and then integrates the specific loudness to measure the total loudness and loudness level. This algorithm also is known as ISO 532B loudness or Zwicker loudness and is in compliance with ISO 532B, DIN 45631, and ISO/R 131.
Time-varying loudness is an algorithm for calculating the loudness of non-steady-state noises, or signals that vary with time. This algorithm measures the 1/3 octave spectrum using exponential averaging with a 2 ms time constant, combines the fractional-octave bands into critical bands, and applies temporal and spectral masking. This algorithm then returns the result as the specific loudness versus critical band rate, integrates the specific loudness, and applies temporal post-masking filters to measure the time-varying loudness. This algorithm calculates the time-varying loudness in compliance with DIN 45631/A.
Roughness is another algorithm used to determine the subjective judgment of sound quality. Roughness correlates to how noticeable or annoying a sound is as heard by the human ear. More specifically, roughness is a hearing sensation related to loudness modulations at frequencies too high to discern separately, such as modulation frequencies greater than 30 Hz.
The roughness algorithm measures the energy in 24 barks, computes and filters the envelope of the signal in each band, measures the amplitude modulation of each envelope, and then weights the level in each band using both the modulation index of that band and a frequency-dependent weighting function. The algorithm returns the result as the roughness spectrum versus critical band rate and then integrates the roughness spectrum to measure the roughness.
Sharpness is a hearing sensation related to frequency and independent of loudness. Sharpness corresponds to the sensation of a sharp, painful, high-frequency sound and is the comparison of the amount of high frequency energy to the total energy. The sharpness algorithm computes sharpness from the sound pressure signal waveform, the 1/3-octave band spectrum calculated over the frequency range 25 Hz to 12.5 kHz, or the specific loudness.
This algorithm normalizes the specific loudness spectrum by the total loudness and weights the spectrum according to frequency. The algorithm returns the frequency-weighted result as the specific sharpness versus critical band rate and then integrates the specific sharpness to measure the sharpness. Higher frequency components in the signal generally result in higher sharpness measurements.
Tonality is used to determine whether a sound consists mainly of tonal components or broadband noise. The algorithm measures the relative strength of the tones in a signal compared to the overall signal. For each time block, this algorithm first varies the frequency resolution according to the frequency selectivity of human hearing, searches the spectrum for likely tones, and then compares the loudness of the tones to the loudness of the sound.
Fluctuation strength is a hearing sensation related to loudness modulations at low frequencies that are discernable individually. Fluctuation strength uses a similar method to "roughness versus time" analysis except that it focuses specifically on signal variations with very low modulation frequencies.
Fluctuation strength measures the energy in 47 overlapping barks, computes and filters the envelope of the signal in each band, measures the amplitude modulation of each envelope, and weights the level in each band using a frequency-dependent weighting function. The algorithm returns the result as the fluctuation strength spectrum versus critical band rate and then integrates the fluctuation strength spectrum to measure the fluctuation strength. The algorithm examines modulations between 0 to 30 Hz, with special emphasis on those near 4 Hz.
The LabVIEW Sound and Vibration Toolkit provides VIs you can use to implement sound quality algorithms. The LabVIEW Sound and Vibration Toolkit also provides in-depth documentation about each of these algorithms. You can download the LabVIEW Sound and Vibration Toolkit for free as a 7-day trial. You also must install LabVIEW in order to use the sound quality algorithms in the LabVIEW Sound and Vibration Toolkit.
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