Application of Feed-Forward Neural Network and MMI-Supervised Vector Quantizer to the Task of Content Based Audio Segmentation by Co-operative Unmanned Flying Robots

This paper deals with the preliminary experiments on general audio segmentation using a MMI-supervised tree-based vector quantizer and feed-forward neural network. This method has been tested with the aim of detection of environmental sounds and speech in a sound stream. The segmentation of an audio...

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Bibliographic Details
Main Authors: Janku, L.S., Hyniova, K.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic testing
Application software
automatic audio segmentation
co-operative unmanned aerial robots
feedforward network
Feedforward neural networks
Feedforward systems
Neural networks
Robots
simulation software NEUSCAR 1.0
Software testing
Software tools
Speech
Streaming media
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Summary:This paper deals with the preliminary experiments on general audio segmentation using a MMI-supervised tree-based vector quantizer and feed-forward neural network. This method has been tested with the aim of detection of environmental sounds and speech in a sound stream. The segmentation of an audio stream is needed for successful localization of speech or environmental sounds in a stream and their possible future classification or even separation. This method has been developed as a preliminary solution of the task of real-world audio signal segmentation by a set of co-operative unmanned flying robots. Application of the proposed method has been tested in simulating software NESCUAR 1.0. (Natural Environment Simulator for Cooperative Unmanned Aerial Robots, version 1.0), a simulating software tool developed by the authors of this paper. The presented method can be also applied separately; its application is not dependent on the simulating software NESCUAR 1.0.
ISSN:2166-0662
2166-0670
DOI:10.1109/ISMS.2010.32