Audio Bank: A High-Level Acoustic Signal Representation for Audio Event Recognition

Tushar Sandhan; Sukanya Sonowal; Jin Young Choi

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arXiv · 2304.05067

Audio Bank: A High-Level Acoustic Signal Representation for Audio Event Recognition

Tushar Sandhan·Sukanya Sonowal·Jin Young Choi

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Abstract

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.

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BibTeX RIS

Tushar Sandhan, Sukanya Sonowal, Jin Young Choi. 2023-04-11. Audio Bank: A High-Level Acoustic Signal Representation for Audio Event Recognition. https://arxiv.org/abs/2304.05067

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