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Proceedings Paper

MP3 compression and transmission of infrasonic sensor array signals and task-specific metrics for distortion evaluation
Author(s): Sergio D. Cabrera; Edward Vidal Jr.; Smitha Paramanandan
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Paper Abstract

Infrasonic sensor arrays are very useful for detecting natural and man-made events. This paper describes part of an ongoing project for compressing and transmitting a set of infrasonic signals that need to be delivered to a remote location for decompression and processing. The project also deals with the evaluation of the effect of the compression distortion on the signals by the use of task-specific distortion metrics. We evaluate the effectiveness of the scheme using one hour worth of signals that were collected during a Space Shuttle launch using a small array of 4 microphones. The approach described here is to combine the 4 signals/channels using a transmultiplexer and to use an off-the-shelf audio compression method, namely the popular MP3 method which is based on subband coding. The transmultiplexer is a 5-channel Cosine-Modulated filterbank from which only the first 4 channels are used.. The codec used in this study is the readily available LAME software package which allows one to choose the output bits per second rate and to turn off the psychoacoustic model. To use an audio coder, the combined signal is first converted to 16 bits per sample and then associated with a 16 KHz. sampling frequency. In the application considered, the microphone signals are used to compute time evolving quantities including: average spectral coherence, beamforming, and phase velocity. These same quantities are used as task-specific metrics that reveal the distortion caused by the application of the MP3 compressor so that the user can evaluate distortion tolerances. From visual evaluation of these metrics we conclude that a compression ratio between 6.4:1 and 8:1 produces negligible distortion in the three task-specific metrics. The beamforming metric is the most sensitive to the compression distortion.

Paper Details

Date Published: 16 September 2005
PDF: 11 pages
Proc. SPIE 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV, 591008 (16 September 2005); doi: 10.1117/12.619319
Show Author Affiliations
Sergio D. Cabrera, Univ. of Texas at El Paso (United States)
Edward Vidal Jr., Army Research Lab. (United States)
Smitha Paramanandan, Univ. of Texas at El Paso (United States)

Published in SPIE Proceedings Vol. 5910:
Advanced Signal Processing Algorithms, Architectures, and Implementations XV
Franklin T. Luk, Editor(s)

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