Speech enhancement techniques using spectral subtraction have the drawback of generating residual noise with a musical character, so-called musical noise. We developed a new post-processing method for suppressing this musical residual noise. In this method, the auditory masking threshold is calculated twice, once before the spectral subtraction and once again afterwards. This ensures that all a...

This paper presents an effective feature processing algorithm for robust speech recognition, based on combined spectral and cepstral processing. The spectral processing consists of FullWave Rectification Spectral Subtraction (FWR-SS) and Likelihood Controlled Instantaneous Noise Estimation (LCINE) while the cepstral processing is based on meanand variance normalisation. The combination is motiv...

A computationally restrained, single-channel, blind dereverberation method is proposed. The proposed method consists of two iterative spectral modifications, which employs spectral subtraction for noise reduction, and a complementary Wiener filter for dereverberation. Modulation transfer function is employed to calculate the dereverberation parameters. Late reverberation is estimated without an...

Speech enhancement is the process of eliminating noise and increasing the quality of a speech signal, which is contaminated with other kinds of distortions. This paper is on developing an optimum cascaded system for speech enhancement. This aim is attained without diminishing any relevant speech information and without much computational and time complexity. LMS algorithm, Spectral Subtraction ...

To improve speech recognition performance in adverse conditions, a noise compensation method is proposed that applies a transformation in the spectral domain whose parameters are optimized based on likelihood of speech GMM modeled on the feature domain. The idea is that additive and convolutional noises have mathematically simple expression in the spectral domain while speech characteristics ar...

In this paper, two short-time spectral amplitude estimators of the speech signal are derived based on a parametric formulation of the original generalized spectral subtraction method. The objective is to improve the noise suppression performance of the original method while maintaining its computational simplicity. The proposed parametric formulation describes the original method and several of...

This article describes a modified technique for enhancing noisy speech to improve automatic speech recognition (ASR) performance. The proposed approach improves the widely used spectral subtraction which inherently suffers from the associated musical noise effects. Through a psychoacoustic masking and critical band variance normalization technique, the artifacts produced by spectral subtraction...

The research work describes the procedure and the development of Isolated Marathi Emotional Speech Database. The database consists of samples, collected from 50 speakers including males and females who simulated the emotions producing by the Marathi utterances which are used in everyday communication and are interpretable in all applied emotions. The speech samples were enhanced by spectral sub...

A novel technique is presented to design the signal subspace speech enhancement based on perceptual post filtering. Firstly, by subspace filter the noisy speech is enhanced. The underlying principle is to decompose the vector space of the noisy signal into a signal plus noise subspace and a noise subspace. The decomposition can theoretically be performed by applying the Karhunen-Loeve transform...

This paper describes an improved spectral subtraction method by using the complementary beamforming microphone array to enhance noisy speech signals for speech recognition. The complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. In this paper, it is shown that the nonlinear subtraction processing with c...