  The present paper introduces a deep neural network (DNN) for predicting the
instantaneous loudness of a sound from its time waveform. The DNN was trained
using the output of a more complex model, called the Cambridge loudness model.
While a modern PC can perform a few hundred loudness computations per second
using the Cambridge loudness model, it can perform more than 100,000 per second
using the DNN, allowing real-time calculation of loudness. The root-mean-square
deviation between the predictions of instantaneous loudness level using the two
models was less than 0.5 phon for unseen types of sound. We think that the
general approach of simulating a complex perceptual model by a much faster DNN
can be applied to other perceptual models to make them run in real time.
