2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST) 2017
DOI: 10.1109/ibcast.2017.7868124
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Performance evaluation of linear and multi-linear subspace learning techniques for object classification based on underwater acoustics

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“…These hand designed features included waveform features, wavelet features, MFCC, Mel-frequency features, nonlinear auditory features, spectral and cepstral features. The two pass split window (TPSW) [32] is applied subsequently after short-time fast Fourier transform for the enhancement of the signal-to-noise ratio (SNR). The TPSW filtering scheme provides a mechanism for obtaining smooth local-mean estimates of the signal.…”
Section: Classification Experimentsmentioning
confidence: 99%
“…These hand designed features included waveform features, wavelet features, MFCC, Mel-frequency features, nonlinear auditory features, spectral and cepstral features. The two pass split window (TPSW) [32] is applied subsequently after short-time fast Fourier transform for the enhancement of the signal-to-noise ratio (SNR). The TPSW filtering scheme provides a mechanism for obtaining smooth local-mean estimates of the signal.…”
Section: Classification Experimentsmentioning
confidence: 99%