  Due to the huge progress of the recording devices, data from heterogeneous
nature can be recorded, such as spatial, temporal and spatio-temporal.
Nowadays, time-based data is of particular interest since it has the ability to
capture the characteristics evolution of the data over time. The temporal data
could be gait, auditory scene, piece of music, and so on. In this paper, we are
particularly interested in feature extraction for two different temporal
recognition applications namely, audio and human behavior analysis and
recognition. Indeed, relevant and discriminative features are of critical and
fundamental importance to achieve high performances in any automatic pattern
recognition system. This work is intended to provide researchers with a brief
overview of the different existing features through an understanding of basic
taxonomies which may serve as a reference to identify the adequate features for
a specific task.
