NVHOEM sound-quality teams / Tier 1 interior trim
Psychoacoustic Sound Quality (SQ) Four-Metric Evaluation
01
Pain Points
- !"Premium vs. cheap" relies on subjective scoring, lacking repeatable objective metrics.
- !A-weighting is too far from the human ear; door-closing and fan sounds need psychoacoustic evaluation.
- !Each of the four metrics is computed with a different tool, and their time series don't align.
02
The Tinia Approach
Compute the four psychoacoustic metrics in parallel, then align and merge them into a composite SQ score; with subjective ratings available, AutoML can distill a discriminant function.
Cut evaluation segmentsLoudness + Sharpness + Roughness + TonalityAlign and mergeComposite SQ score
03
Nodes Used
Audio Segment Splitaudio_segment_split
Cut out the evaluation segmentsLoudnessloudness
Loudness (sone) time seriesSharpnesssharpness
Sharpness (acum)Roughnessroughness
Roughness (asper)Tonalitytonality
Tonality metricFeature Mergefeature_merge
Align and merge the four metric time seriesChart Viewerchart_viewer
Four metric curves + composite scoreCore nodes; add or remove steps as needed for your data and standards — the node graph is always editable.
04
Expected Output
- Time series for all four metrics: Loudness / Sharpness / Roughness / Tonality.
- A composite sound-quality score that replaces part of the subjective evaluation.
- Optional: a subjective-objective regression discriminant function (AutoML distillation).
Reference Standards / Engineering PracticeISO 532 series · ECMA-418-2 · DIN 45631/45692
Want to run this workflow on your own data?
Request a Trial