Short-time Fourier Domain Research Articles

Enhancement of speech signal using diminished empirical mean curve decomposition-based adaptive Wiener filtering

During the last few decades, speech signal enhancement has been one of the wide-spreading research topics. Numerous algorithms are being proposed to enhance the perceptibility and the quality of speech signal. These algorithms are often formulated to recover the clear signal from the signals that are ruined by noise. Usually, short-time Fourier transform and wavelet transform are widely used to process the speech signal. This paper attempts to overcome the regular drawbacks of the speech enhancement algorithms. As the frequency domain has good noise-removing ability, the short-time Fourier domain is also aimed to enhance the speech. Additionally, this paper introduces a decomposition model, named diminished empirical mean curve decomposition, to adaptively tune the Wiener filtering process and to accomplish effective speech enhancement. The performances of the proposed method and the conventional methods are compared, and it is observed that the proposed method is superior to the conventional methods.

Solving Time-Domain Audio Inverse Problems Using Nonnegative Tensor Factorization

Nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) are important tools for modeling nonnegative data, which gained increasing popularity in various fields, a significant one of which is audio processing. However, there are still many problems in audio processing, for which the NMF (or NTF) model has not been successfully utilized. In this paper, we propose a new algorithm based on the NMF (and NTF) in the short-time Fourier domain for solving a large class of audio inverse problems with missing or corrupted time-domain samples. The proposed approach overcomes the difficulty of employing a model in the frequency domain to recover time-domain samples with the help of probabilistic modeling. Its performance is demonstrated for the following applications: audio declipping and declicking (never solved with NMF/NTF modeling prior to this paper); joint audio declipping/declicking and source separation (never solved with NMF/NTF modeling or any other method prior to this paper); and compressive sampling recovery and compressive sampling-based informed source separation (an extremely low complexity encoding scheme that is possible with the proposed approach and has never been proposed prior to this paper).