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Recent trends show a growing demand for improved powertrain NVH and sound quality. In particular, there is little customer acceptance of tonal annoyances under any driving condition. Thus, powertrain NVH and product development engineers have a strong need to confidently determine acceptable noise levels for commodities that produce narrow band noise. Components such as power steering, transmission gears, pumps, engine timing chains, axle gearing, etc., all may produce significant tones under various vehicle conditions. The perception of the tone is highly influenced by its frequency and background noise. Background noise is composed of wind, road, and engine noise. A methodology and toolset of masking perception algorithms has been developed to meet these needs. The Masking Perception Analysis Software (MPAS) is used to address the development and verification of acceptable powertrain tonal levels as well as the diagnosis of tonal-related issues.

Many engine tick and knock issues are clearly audible, yet cannot be characterized by common sound quality metrics such as time-varying loudness, sharpness, fluctuation strength, or roughness. This paper summarizes the recent development and application of an objective metric that agrees with subjective impressions of impulsive engine noise. The metric is based on a general impulsive noise model [1], consisting of a psychoacoustic processing stage followed by a transient detection stage. The psychoacoustic stage is extracted from portions of a time-varying loudness model. The primary output of the impulsive engine noise model is a time series that indicates the location and “intensity” of impulsive engine noise events. The information in this time series is reduced either to a single number metric, or to a frequency-based vector of numbers that indicates the amount of impulsiveness in the recorded sound.