  Automatic audio event recognition plays a pivotal role in making human robot
interaction more closer and has a wide applicability in industrial automation,
control and surveillance systems. Audio event is composed of intricate phonic
patterns which are harmonically entangled. Audio recognition is dominated by
low and mid-level features, which have demonstrated their recognition
capability but they have high computational cost and low semantic meaning. In
this paper, we propose a new computationally efficient framework for audio
recognition. Audio Bank, a new high-level representation of audio, is comprised
of distinctive audio detectors representing each audio class in
frequency-temporal space. Dimensionality of the resulting feature vector is
reduced using non-negative matrix factorization preserving its discriminability
and rich semantic information. The high audio recognition performance using
several classifiers (SVM, neural network, Gaussian process classification and
k-nearest neighbors) shows the effectiveness of the proposed method.
