  Classification systems are normally trained by minimizing the cross-entropy
between system outputs and reference labels, which makes the Kullback-Leibler
divergence a natural choice for measuring how closely the system can follow the
data. Precision and recall provide another perspective for measuring the
performance of a classification system. Non-binary references can arise from
various sources, and it is often beneficial to use the soft labels for training
instead of the binarized data. However, the existing definitions for precision
and recall require binary reference labels, and binarizing the data can cause
erroneous interpretations. We present a novel method to calculate precision,
recall and F-score without quantizing the data. The proposed metrics extend the
well established metrics as the definitions coincide when used with binary
labels. To understand the behavior of the metrics we show simple example cases
and an evaluation of different sound event detection models trained on real
data with soft labels.
