Separation of human and animal seismic signatures using non-negative matrix factorization

Seismic footstep detection based systems can be employed for homeland security applications such as perimeter protection and the border security. This paper reports an approach based on non-negative matrix factorization (NMF) for seismic footstep signal separation for a single channel recording. A supervised NMF technique is employed to separate the human footstep signatures from the horse footstep signatures. The proposed algorithm is applied on the spectrogram of human footstep signals and horse footstep signals. The spectrograms of these signals are presented as a sum of components, each having a fixed spectrum and time-varying gain. The main benefit of the proposed technique is its ability to decompose a complex signal automatically into objects that have a meaningful interpretation. In this paper, a sparsity-based NMF algorithm is developed and implemented on seismic data of human and horse footsteps. The performance of this method is very promising and is demonstrated by the experimental results.

► Non-negative matrix technique is employed to separate a human from a horse.
► The applications of the algorithms are: perimeter and border protection.
► It can be used in conjunction with classification technique for better discrimination.