  Respiratory sound classification is an important tool for remote screening of
respiratory-related diseases such as pneumonia, asthma, and COVID-19. To
facilitate the interpretability of classification results, especially ones
based on deep learning, many explanation methods have been proposed using
prototypes. However, existing explanation techniques often assume that the data
is non-biased and the prediction results can be explained by a set of
prototypical examples. In this work, we develop a unified example-based
explanation method for selecting both representative data (prototypes) and
outliers (criticisms). In particular, we propose a novel application of
adversarial attacks to generate an explanation spectrum of data instances via
an iterative fast gradient sign method. Such unified explanation can avoid
over-generalisation and bias by allowing human experts to assess the model
mistakes case by case. We performed a wide range of quantitative and
qualitative evaluations to show that our approach generates effective and
understandable explanation and is robust with many deep learning models
