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The efficiency of Hilbert spectrum (HS) in time-frequency representation (TFR) of audio signals is investigated in this paper. HS is derived by applying empirical mode decomposition (EMD), a newly developed data adaptive method for nonlinear and non-stationary signal analysis together with Hilbert transform. EMD represents any time domain signal as a sum of a finite number of bases called intrinsic...
This paper presents a new method of periodic/non-periodic (P/nP) classification of noisy speech signals. Empirical mode decomposition (EMD), a newly developed tool to analyze nonlinear and non-stationary signals is used to filter the additive noise with the speech signal. The normalized autocorrelation of the filtered speech signal is computed to enhance the periodicity of the analyzing speech signal...
This paper presents a new technique for voiced/unvoiced (V/UV) discrimination based on the extraction of pitch period. Empirical mode decomposition (EMD) is employed for multi-band representation of speech signal in time domain. The fundamental oscillation in a speech segment is determined in the autocorrelation function (ACF) of the EMD space. A damped cosine model is fitted using least squared method...
This paper presents a method of audio signal separation from stereo mixtures using binary masking in time-frequency (TF) domain based on the spatial location of the audio sources. The TF representation of audio signal is obtained by Hubert spectrum (HS). The Hubert transformation together with empirical mode decomposition (EMD) produces HS which is a fine-resolution TF representation of any nonlinear...
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