Identification and Classification of Noise Patterns

Abstract

Sound quality evaluation is a challenge due to spectral and temporal structures of noise. Tonal components, howling sounds and modulated signals are often the cause of customer complaints. Thus, besides frequency‐weighted level like dB(A) or loudness, additional parameters are required. Besides time‐varying loudness, other psychoacoustic parameters like sharpness and roughness can be used for sound quality evaluation. Sharpness considers the amount of high frequency components of a noise, and roughness evaluates modulation characteristics. In addition, a metric combining modulation spectral analysis with loudness calculation has been introduced. Some years ago, a "Hearing Model" was developed with the intention of explaining and describing psychoacoustic effects. Applying the Hearing Model to sound quality tasks allows evaluating the spectral and temporal patterns of a sound ("Relative Approach" analysis) where absolute level or loudness is often without significance. The Relative Approach analysis emphasizes all relevant signal components concerning human auditory perception: tonal and transient signals. For extracting and evaluating individual patterns further signal‐processing steps are necessary. The paper presents different methods for effective sound quality evaluation of noise and their application to several examples.