The measurement of perceived loudness is a difficult yet im-portant task with a multitude of applications such as loudness align-ment of complex stimuli and loudness restoration for the hear-ing impaired. Although computational hearing models exist, few are able to accurately predict the binaural loudness of everyday sounds. Such models demand excessive processing power making real-time loudness metering problematic. In this work, the dy-namic auditory loudness models of Glasberg and Moore (J. Audio Eng. Soc., 2002) and Chen and Hu (IEEE ICASSP, 2012) are pre-sented, extended and realised as binaural loudness meters. The performance bottlenecks are identified and alleviated by reducing the complexity of the excitation transformation stages. The ef-fects of three parameters (hop size, spectral compression and filter spacing) on model predictions are analysed and discussed within the context of features used by scientists and engineers to quantify and monitor the perceived loudness of music and speech. Parame-ter values are presented and perceptual implications are described. 1
