Speech Enhancement with Adaptive Thresholding and Kalman Filtering

Speech enhancement has been extensively studied for many years and various speech enhance- ment methods have been developed during the past decades. One of the objectives of speech en- hancement is to provide high-quality speech communication in the presence of background noise and concurrent interference signals. In the process of speech communication, the clean speech sig- nal is inevitably corrupted by acoustic noise from the surrounding environment, transmission media, communication equipment, electrical noise, other speakers, and other sources of interference. These disturbances can significantly degrade the quality and intelligibility of the received speech signal. Therefore, it is of great interest to develop efficient speech enhancement techniques to recover the original speech from the noisy observation. In recent years, various techniques have been developed to tackle this problem, which can be classified into single channel and multi-channel enhancement approaches. Since single channel enhancement is easy to implement, it has been a significant field of research and various approaches have been developed. For example, spectral subtraction and Wiener filtering, are among the earliest single channel methods, which are based on estimation of the power spectrum of stationary noise. However, when the noise is non-stationary, or there exists music noise and ambient speech noise, the enhancement performance would degrade considerably. To overcome this disadvantage, this thesis focuses on single channel speech enhancement under adverse noise environment, especially the non-stationary noise environment. Recently, wavelet transform based methods have been widely used to reduce the undesired background noise. On the other hand, the Kalman filter (KF) methods offer competitive denoising results, especially in non-stationary environment. It has been used as a popular and powerful tool for speech enhancement during the past decades. In this regard, a single channel wavelet thresholding based Kalman filter (KF) algorithm is proposed for speech enhancement in this thesis. The wavelet packet (WP) transform is first applied to the noise corrupted speech on a frame-by-frame basis, which decomposes each frame into a number of subbands. A voice activity detector (VAD) is then designed to detect the voiced/unvoiced frames of the subband speech. Based on the VAD result, an adaptive thresholding scheme is applied to each subband speech followed by the WP based reconstruction to obtain the pre-enhanced speech. To achieve a further level of enhancement, an iterative Kalman filter (IKF) is used to process the pre-enhanced speech. The proposed adaptive thresholding iterative Kalman filtering (AT-IKF) method is evaluated and compared with some existing methods under various noise conditions in terms of segmental SNR and perceptual evaluation of speech quality (PESQ) as two well-known performance indexes. Firstly, we compare the proposed adaptive thresholding (AT) scheme with three other threshold- ing schemes: the non-linear universal thresholding (U-T), the non-linear wavelet packet transform thresholding (WPT-T) and the non-linear SURE thresholding (SURE-T). The experimental results show that the proposed AT scheme can significantly improve the segmental SNR and PESQ for all input SNRs compared with the other existing thresholding schemes. Secondly, extensive computer simulations are conducted to evaluate the proposed AT-IKF as opposed to the AT and the IKF as standalone speech enhancement methods. It is shown that the AT-IKF method still performs the best. Lastly, the proposed ATIKF method is compared with three representative and popular meth- ods: the improved spectral subtraction based speech enhancement algorithm (ISS), the improved Wiener filter based method (IWF) and the representative subband Kalman filter based algorithm (SIKF). Experimental results demonstrate the effectiveness of the proposed method as compared to some previous works both in terms of segmental SNR and PESQ

Studies in Signal Processing Techniques for Speech Enhancement: A comparative study

Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands

Single-channel speech enhancement using implicit Wiener filter for high-quality speech communication

Speech enables easy human-to-human communication as well as human-to-machine interaction. However, the quality of speech degrades due to background noise in the environment, such as drone noise embedded in speech during search and rescue operations. Similarly, helicopter noise, airplane noise, and station noise reduce the quality of speech. Speech enhancement algorithms reduce background noise, resulting in a crystal clear and noise-free conversation. For many applications, it is also necessary to process these noisy speech signals at the edge node level. Thus, we propose implicit Wiener filter-based algorithm for speech enhancement using edge computing system. In the proposed algorithm, a first order recursive equation is used to estimate the noise. The performance of the proposed algorithm is evaluated for two speech utterances, one uttered by a male speaker and the other by a female speaker. Both utterances are degraded by different types of non-stationary noises such as exhibition, station, drone, helicopter, airplane, and white Gaussian stationary noise with different signal-to-noise ratios. Further, we compare the performance of the proposed speech enhancement algorithm with the conventional spectral subtraction algorithm. Performance evaluations using objective speech quality measures demonstrate that the proposed speech enhancement algorithm outperforms the spectral subtraction algorithm in estimating the clean speech from the noisy speech. Finally, we implement the proposed speech enhancement algorithm, in addition to the spectral subtraction algorithm, on the Raspberry Pi 4 Model B, which is a low power edge computing device.publishedVersio

Speech Enhancement By Exploiting The Baseband Phase Structure Of Voiced Speech For Effective Non-Stationary Noise Estimation

Speech enhancement is one of the most important and challenging issues in the speech communication and signal processing field. It aims to minimize the effect of additive noise on the quality and intelligibility of the speech signal. Speech quality is the measure of noise remaining after the processing on the speech signal and of how pleasant the resulting speech sounds, while intelligibility refers to the accuracy of understanding speech. Speech enhancement algorithms are designed to remove the additive noise with minimum speech distortion.The task of speech enhancement is challenging due to lack of knowledge about the corrupting noise. Hence, the most challenging task is to estimate the noise which degrades the speech. Several approaches has been adopted for noise estimation which mainly fall under two categories: single channel algorithms and multiple channel algorithms. Due to this, the speech enhancement algorithms are also broadly classified as single and multiple channel enhancement algorithms.In this thesis, speech enhancement is studied in acoustic and modulation domains along with both amplitude and phase enhancement. We propose a noise estimation technique based on the spectral sparsity, detected by using the harmonic property of voiced segment of the speech. We estimate the frame to frame phase difference for the clean speech from available corrupted speech. This estimated frame-to-frame phase difference is used as a means of detecting the noise-only frequency bins even in voiced frames. This gives better noise estimation for the highly non-stationary noises like babble, restaurant and subway noise. This noise estimation along with the phase difference as an additional prior is used to extend the standard spectral subtraction algorithm. We also verify the effectiveness of this noise estimation technique when used with the Minimum Mean Squared Error Short Time Spectral Amplitude Estimator (MMSE STSA) speech enhancement algorithm. The combination of MMSE STSA and spectral subtraction results in further improvement of speech quality

Comparison of Speech Enhancement Algorithms

The simplest and very familiar method to take out stationary background noise is spectral subtraction. In this algorithm, a spectral noise bias is calculated from segments of speech inactivity and is subtracted from noisy speech spectral amplitude, retaining the phase as it is. Secondary procedures follow spectral subtraction to reduce the unpleasant auditory effects due to spectral error. The drawback of spectral subtraction is that it is applicable to speech corrupted by stationary noise. The research in this topic aims at studying the spectral subtraction & Wiener filter technique when the speech is degraded by non-stationary noise. We have studied both algorithms assuming stationary noise scenario. In this we want to study these two algorithms in the context of non-stationary noise. Next, decision directed (DD) approach, is used to estimate the time varying noise spectrum which resulted in better performance in terms of intelligibility and reduced musical noise. However, the a priori SNR estimator of the current frame relies on the estimated speech spectrum from the earlier frame. The undesirable consequence is that the gain function doesn’t match the current frame, resulting in a bias which causes annoying echoing effect. A method called Two-step noise reduction (TSNR) algorithm was used to solve the problem which tracks instantaneously the non-stationarity of the signal but, not by losing the advantage of the DD approach. The a priori SNR estimation was modified and made better by an additional step for removing the bias, thus eliminating reverberation effect. The output obtained even with TSNR still suffers from harmonic distortions which are inherent to all short time noise suppression techniques, the main reason being the inaccuracy in estimating PSD in single channel systems. To outdo this problem, a concept called, Harmonic Regeneration Noise Reduction (HRNR) is used wherein a non-linearity is made use of for regenerating the distorted/missing harmonics. All the above discussed algorithms have been implemented and their performance evaluated using both subjective and objective criteria. The performance is significantly improved by using HRNR combined with TSNR, as compared to TSNR, DD alone, as HRNR ensures restoration of harmonics. The spectral subtraction performance stands much below the above discussed methods for obvious reasons

Comparison of Speech Enhancement Algorithms

AbstractThe simplest and very familiar method to take out stationary background noise is spectral subtraction. In this algorithm, a spectral noise bias is calculated from segments of speech inactivity and is subtracted from noisy speech spectral amplitude, retaining the phase as it is. Secondary procedures follow spectral subtraction to reduce the unpleasant auditory effects due to spectral error. The drawback of spectral subtraction is that it is applicable to speech corrupted by stationary noise. The research in this topic aims at studying the spectral subtraction & Wiener filter technique when the speech is degraded by non-stationary noise. We have studied both algorithms assuming stationary noise scenario. In this we want to study these two algorithms in the context of non-stationary noise. Next, decision directed (DD) approach, is used to estimate the time varying noise spectrum which resulted in better performance in terms of intelligibility and reduced musical noise. However, the a priori SNR estimator of the current frame relies on the estimated speech spectrum from the earlier frame. The undesirable consequence is that the gain function doesn’t match the current frame, resulting in a bias which causes annoying echoing effect. A method called Two-step noise reduction (TSNR) algorithm was used to solve the problem which tracks instantaneously the non-stationarity of the signal but, not by losing the advantage of the DD approach. The a priori SNR estimation was modified and made better by an additional step for removing the bias, thus eliminating reverberation effect. The output obtained even with TSNR still suffers from harmonic distortions which are inherent to all short time noise suppression techniques, the main reason being the inaccuracy in estimating PSD in single channel systems. To outdo this problem, a concept called, Harmonic Regeneration Noise Reduction (HRNR) is used wherein a non-linearity is made use of for regenerating the distorted/missing harmonics. All the above discussed algorithms have been implemented and their performance evaluated using both subjective and objective criteria. The performance is significantly improved by using HRNR combined with TSNR, as compared to TSNR, DD alone, as HRNR ensures restoration of harmonics. The spectral subtraction performance stands much below the above discussed methods for obvious reasons