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ISO 1996-1:2003

ISO 1996-1:2003 defines the basic quantities to be used for the description of noise in community environments and describes basic assessment procedures. It also specifies methods to assess environmental noise and gives guidance on predicting the potential annoyance response of a community to long-term exposure from various types of environmental noises. The sound sources can be separate or in various combinations. Application of the method to predict annoyance response is limited to areas where people reside and to related long-term land uses.

Reference

ISO 1996-1:2003: Description, measurement and assessment of environmental noise

Functions

composite_rating_level 

composite_rating_level(
    levels: ndarray, hours: ndarray, adjustment: ndarray
) -> float | ndarray

Composite rating level.

The composite rating level is calculated as:

\[ L_R = 10 \log{\left[ \sum_i \frac{d_i}{24} 10^{(L_i+K_i)/10} \right]} \]

where \(i\) is a period. See equation 6 and 7 of the standard.

Note

Summation is done over the last axis

PARAMETER DESCRIPTION
levels

Level per period

TYPE: ndarray

hours

Amount of hours per period

TYPE: ndarray

adjustment

Adjustment per period

TYPE: ndarray

RETURNS DESCRIPTION
float | ndarray

The composite rating level in dB as a float if a scalar result is obtained, or as
float | ndarray

an ndarray if multiple periods are processed.
Source code in acoustic_toolbox/standards/iso_1996_1_2003.py 
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def composite_rating_level(
    levels: np.ndarray, hours: np.ndarray, adjustment: np.ndarray
) -> float | np.ndarray:
    r"""Composite rating level.

    The composite rating level is calculated as:

    $$
    L_R = 10 \log{\left[ \sum_i \frac{d_i}{24} 10^{(L_i+K_i)/10}  \right]}
    $$

    where $i$ is a period. See equation 6 and 7 of the standard.

    Note:
      Summation is done over the last axis

    Args:
        levels: Level per period
        hours: Amount of hours per period
        adjustment: Adjustment per period

    Returns:
        The composite rating level in dB as a float if a scalar result is obtained, or as
        an ndarray if multiple periods are processed.

    """
    levels = np.asarray(levels)
    hours = np.asarray(hours)
    adjustment = np.asarray(adjustment)

    return 10.0 * np.log10(
        (hours / 24.0 * 10.0 ** ((levels + adjustment) / 10.0)).sum(axis=-1)
    )

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