Calculating Sound Pressure Levels and Psychoacoustic Parameters of Audio Signals

To conduct comprehensive analysis and interpretation of audio signals, several key acoustic parameters must be evaluated. The total sound pressure level serves as a fundamental metric for quantifying overall signal intensity, typically calculated using root-mean-square (RMS) pressure values across the entire frequency spectrum. Implementation often involves applying the formula Lp = 20*log10(p_rms/p_ref) where p_ref is the standard reference pressure of 20 μPa. The 1/3 octave band sound pressure levels provide detailed frequency-specific information, enabling identification of dominant frequency components through band-pass filtering techniques. Code implementation typically requires designing ISO-standard fractional octave filters or using signal processing toolkits like Python's SciPy or MATLAB's Signal Processing Toolbox for spectral decomposition. Total loudness represents perceptual loudness perception according to human hearing characteristics, commonly computed using standardized models like ISO 532-1 (Zwicker method). Algorithm implementation involves frequency weighting and integration across critical bands, often requiring Bark scale transformation and summation of specific loudness contributions. Characteristic loudness captures temporal variations in auditory perception, implemented through time-domain analysis and sliding window techniques to monitor loudness fluctuations. This may involve short-term Fourier transforms or psychoacoustic analysis frameworks that account for temporal masking effects. Sharpness quantification identifies high-frequency dominance in sound quality assessment, typically calculated using weighting functions that emphasize frequencies above 2 kHz. Implementation commonly applies Bismarck's model or Aures' formula, integrating specific loudness values with frequency-dependent weighting factors. Integrating these parameters through modular code architecture allows for comprehensive audio signal characterization, facilitating optimization for specific applications such as noise control, audio engineering, or product sound design. Practical implementation might involve object-oriented programming with separate classes for each psychoacoustic metric calculation.