Single Channel Speech Enhancement Using Spectral Subtraction Based on Minimum Statistics (original) (raw)
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Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique
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Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable i...
Enhanced spectral subtraction method for noise reduction with minimal speech distortion
In this paper, we propose a method for enhancing speech corrupted by broadband noise. The method is based on the spectral subtraction technique. Besides reducing the noise conventional spectral subtraction introduces an annoying residual musical noise. To eliminate the musical noise we propose to introduce reduced varying scaling factor of spectral subtraction, with a following application of weighted function. Weighting function, used in the proposed algorithm, attenuates frequency spectrum components lying outside identified formants regions. Algorithm effects a substantial reduction of the musical noise without significantly distorting the speech. Listening tests were performed to determinate the subjective quality and intelligibility of speech enhanced by our method.
Effect of Speech enhancement using spectral subtraction on various noisy environment
IRJET, 2022
Analysis Modification Synthesis (AMS) plays a key role in many audio signal processing applications, separating the audio stream into time intervals with speech activity and time intervals without speech. Many features have been introduced into the literature that reflect the existence of language. Therefore, this article presents a structured overview of several established speech enhancement features targeting different characteristics of speech. Categorize features in terms of their exploitable properties. B. Evaluate performance in a background noise environment, different input SNR categories, and some dedicated functions. Our analysis shows how to select promising VAD features and find reasonable tradeoffs between performance and complexity. To estimate clean speech using the Fast Fourier Transform (FFT), we emphasize the noise spectrum estimated during speech, subtract it from the noisy speech spectrum, and consider the average amplitude of the clean spectrum. and tried to develop a new method to minimize the spectrum of loud sounds. The noise reduction algorithm uses MATLAB software to semi- duplicate the noisy speech data (overlap-add processing) and use FFT to calculate the corresponding amplitude spectrum to remove noise from the noisy speech. and performed by reversing the audio in time. Reconstructed with the Inverse Fast Fourier Transform (IFFT).
SPEECH ENHANCEMENT USING SPECTRAL SUBTRACTION TECHNIQUE WITH MINIMIZED CROSS SPECTRAL COMPONENTS
The aim of speech enhancement is to get significant reduction of noise and enhanced speech from noisy speech. There are several approaches for speech enhancement .earlier approaches didn't consider cross spectral terms into account. Cross spectral terms become prominent when processing window size becomes small i.e. 20ms-30ms. In this paper, an enhancement method is proposed for significant reduction of noise, and improvement in the quality and perceptibility of speech degraded by correlated additive background noise. The proposed method is based on the spectral subtraction technique. The simple spectral subtraction technique results in poor reduction of noise. One of the main reasons for this is neglecting the cross spectral terms of speech and noise, based on the appropriation that clean speech and noise signals are completely uncorrelated to each other, which is not true on short time basis. In this paper an improvement in reduction of the noise is achieved as compared to the earlier methods. This fact is mainly attributed to the cross spectral terms between speech and noise. This algorithm can be implemented and used in hearing aids for the benefit of hearing impaired people. Objective speech quality measures, spectrogram analyses and subjective listening tests conforms the proposed method is more effective in comparison with earlier speech enhancement techniques.
2011
Enhancement of speech signal degraded by additive background noise has received more attention over the past decade, due to wide range of applications and limitations of the available methods. Main objective of speech enhancement is to improve the perceptual aspects of speech such as overall quality, intelligibility and degree of listener fatigue. Among the all available methods the spectral subtraction algorithm is the historically one of the first algorithm, proposed for background noise reduction. The greatest asset of Spectral Subtraction Algorithm lies in its simplicity. The simple subtraction process comes at a price. More papers have been written describing variations of this algorithm that minimizes the shortcomings of the basic method than other algorithms. In this paper we present the review of basic spectral subtraction Algorithm, a short coming of basic spectral subtraction Algorithm, different modified approaches of Spectral Subtraction Algorithms such as Spectral Subtr...
Review of Spectral Subtraction Techniques for Speech Enhancement
2011
Speech enhancement aims to improve speech quality by using various techniques and algorithms. The Spectral subtraction technique is historically one of the first algorithms proposed for removal of additive background noise. It is a single channel speech enhancement technique for the enhancement of speech degraded by additive background noise. Background noise can effect our conversation in a noisy environment like in streets or in a car, when sending speech from the cockpit of an airplane to the ground or to the cabin and can effect both quality and intelligibility of speech. With the passage of time Spectral subtraction has undergone many modifications. This is a review paper and its objective is to provide an overview of the variety of spectral subtraction techniques that have been proposed for enhancement of speech degraded by additive background noise during past decades . Section I gives the Introduction to Speech enhancement and explain basic Spectral Subtraction technique. Se...
Developments in Spectral Subtraction for Speech Enhancement
2012
Speech enhancement aims to improve speech quality by using various techniques and algorithms. Over the past several years there has been considerable attention focused on the problem of enhancement of speech degraded by additive background noise. Background noise suppression has many applications. Using mobile in a noisy environment like in streets or in a car is an obvious application, removing the background noise when sending speech from the cockpit of an airplane to the ground or to the cabin. The spectral subtractive algorithm is historically one of the first algorithms proposed for additive background noise and it has gone through many modifications with time. This is a review paper and its objective is to provide an overview of the variety of spectral subtraction techniques that have been proposed for enhancement of speech degraded by additive background noise during past decades . Section I gives the Introduction to Speech enhancement and explain basic Spectral Subtraction t...
Review of Spectral Subtraction Techniques for Speech Enhancement 1
2012
Speech enhancement aims to improve speech quality by using various techniques and algorithms. The Spectral subtraction technique is historically one of the first algorithms proposed for removal of additive background noise. It is a single channel speech enhancement technique for the enhancement of speech degraded by additive background noise. Background noise can effect our conversation in a noisy environment like in streets or in a car, when sending speech from the cockpit of an airplane to the ground or to the cabin and can effect both quality and intelligibility of speech. With the passage of time Spectral subtraction has undergone many modifications. This is a review paper and its objective is to provide an overview of the variety of spectral subtraction techniques that have been proposed for enhancement of speech degraded by additive background
World Wide Journal of Multidisciplinary Research and Development, 2016
Speech enhancement entails a process of reducing noise and distortions by increasing the quality and intelligibility of a speech signal. This paper presents evaluation of spectral subtraction algorithm for noisy speech (samples taken in an environment where electricity generator is operated) without losing any part of the speech signal in terms of quality, quantity and without much computational and time complexity enhancement at different signal to noise ratios (SNR). Spectral subtraction was carried out on noisy speech samples at different SNR. The Noise removal algorithm was implemented using Matlab software. The corresponding spectrum was computed using the DFT (Discrete Fourier Transform) which removes the noise from the noisy speech and the corresponding spectrum was reconstructed in the time domain using the Inverse Discrete Fourier Transform (IDFT). The algorithms performance was evaluated by varying the Signal to Noise Ratio (SNR). The result indicates the optimal SNR values for electric generator noisy Speech Samples at-5dB, 5dB, 10Db, 15Db and 20dB. The spectral subtraction algorithms perform excellently in SNR range of-5.0000dB to 17.0500dB without any loss of part of the speech signal. Introduction Enhancing the quality and intelligibility of noisy speech signal has attracted the interest of researchers over the years. The goal has been to improve quality, intelligibility and degree of listener fatigue of the speech signal [1]–[5]. Speech enhancement is an aspect of speech processing [6] used to manage the effects of noise. And it can be classified into, single channel, dual channel or multi-channel enhancement. Although the performance of multi-channel speech enhancement is better than that of single channel enhancement [7] , the single channel speech enhancement is still a significant field of research interest because of its simple implementation and ease of computation. In single channel applications, only a single microphone is available and the characterization of noise statistics is extracted during the periods of pauses, which requires a stationary assumption of the background noise. Among speech enhancement techniques popular among researchers is the spectral subtraction techniques. In spectral subtraction, noise spectrum is estimated at silence region at the start of the speech, with the assumption that noise will be stationary throughout the speech, and this is not true in practice [1]. The estimation of the spectral amplitude of the noise data is easier than estimation of both the amplitude and phase. In [8] , it is revealed that the short-time spectral amplitude (STSA) is more important than the phase information for the quality and intelligibility of speech. Based on the STSA estimation, the single channel enhancement technique can be divided into two classes. The first class attempts to estimate the short-time spectral magnitude of the speech by subtracting a noise estimate. The noise is estimated during speech pauses of the noisy speech [8]. The second class applies a spectral subtraction filter (SSF) to the noisy speech, so that the spectral amplitude of enhanced speech can be obtained. The design principle is to select appropriate parameters of the filter to minimize the difference between the enhanced speech and the clean speech. These two classes belong to the family of spectral subtractive-type algorithms [9]–[12]. The spectral subtraction method of single channel speech enhancement is the most widely used
The Spectral Subtractive-Type Algorithms for Enhancement of Noisy Speech: A Review
International Journal of …, 2011
The spectral subtraction method is a classical approach for enhancement of speech degraded by additive background noise. The basic principle of this method is to estimate the short-time spectral magnitude of speech by subtracting estimated noise spectrum from the noisy speech spectrum. This is also achieved by multiplying the noisy speech spectrum with a gain function and later combining it with the phase of the noisy speech. Besides reducing the background noise, this method introduces an annoying perceptible tonal characteristic in the enhanced speech and affects the human listening, known as remnant musical noise. Several variations and implementations of this method have been adopted in past decades to address the limitations of spectral subtraction method. These variations constitute a family of subtractive-type algorithms and operate in frequency domain. The objective of this paper is to provide an extensive overview of spectral subtractive-type algorithms for enhancement of noisy speech. After the review, this paper is concluded by mentioning a future direction of speech enhancement research from spectral subtraction perspective.