Abstract
S-transform is an effective time-frequency representation which gives simultaneous frequency and time distribution information alike the wavelet transforms (WT). However, the ST redundantly doubles the dimension of the original data set and the Discrete Orthonormal S-Transform (DOST) can decrease the redundancy of S-transform farther. So, this paper aims to propose a new method to remove additive background noise from noisy speech signal using DOST which supplies a multi-resolution analysis (MRA) spatial-frequency representation of image processing and signal analysis. Hence, the performances of the applied speech enhancement technique have been evaluated objectively and subjectively in comparison with respect to many other methods in four background noises at different SNR levels.
Highlights
The distortion of signals by noise is a ubiquitous problem
In order to evaluate the denoising performance of the Discrete Orthonormal S-Transform (DOST) method and to compare it to Discrete Wavelet Transform (DWT) denoising and Spectral Subtraction; a number of objective tests used for speech enhancement technique evaluation, are presented in this study
The evaluation of the proposed technique is performed by comparing it to the speech enhancement technique based on DWT and the technique based on Spectral Subtraction
Summary
The background noise deteriorates the intelligibility and quality of the speech signals resulting in a harsh drop in performance of speech applications such as sound recording, telecommunications and teleconferencing. These applications need noise reduction and recover the clean signal from noisy signal. Speech enhancement is the most important technique in speech signal processing domain. It eliminates noise and ameliorates the quality and intelligibility of speech communication. Noise suppression from speech signals is a very interesting area of researchers during speech processing
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