Spectral Subtraction Algorithm Research Articles

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Abstract To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction.

Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound. In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e.,mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR). Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

A comparative study of multiple music signal noise reduction algorithms in music processing

Electronic music is susceptible to noise in production, which needs to be processed. This paper analyzed several commonly used noise reduction algorithms, including wiener filtering, wavelet transform, spectral subtraction, and improved spectral subtraction, and then compared the noise reduction performance of several algorithms by producing noisy music datasets in the audio analysis tool librosa. It was found from the experimental results that the wavelet transform algorithm performed best when sym3 was used as the wavelet basis function, and the number of decomposition layers was 7. The comparison of different algorithms showed that the performance of the wiener filtering algorithm was poor in reducing noise, and the signal-to-noise ratio (SNR) and signal distortion ratio (SDR) was low; the performance of the improved spectral subtraction algorithm was the best, and the SNR and SDR were 20.36 dB and 17.94 dB, respectively, when the SNR was −8 dB, which were better than the other algorithms. The experimental results demonstrate the reliability of the improved spectral subtraction method in music signal noise reduction. The algorithm can be applied in practical music processing.

Comparison of spectral subtraction noise reduction algorithms

Noise in media is any undesirable signal that masks relevant information content. The addition of noise to real-world data in any context is practically inevitable. Noise reduction algorithms in the past have addressed the problem but lacked adaptability to various real-world applications while also being time and resource extensive. Spectral subtraction provides a hybrid approach to noise reduction that incorporates versatility and efficient resource usage. This research tested the performance of two spectral subtraction noise reduction algorithms (stationary and non-stationary) across five categories of real-world noise (speech only, speech with natural noise, music, animal sounds, and noise only). The research question under study was how stationary and non-stationary spectral subtraction algorithms differ in their noise reduction performance when subjected to the various categories of noise. The testing was done based on normalized cross-correlation, which is the similarity between the noise-reduced audio and the original recording in each case. Non-stationary spectral subtraction performed better in samples where human speech was the target: speech only and speech with natural noise. Stationary spectral subtraction performed better when denoising music and animal sounds. This anomaly in performance between the two algorithms was only noted in categories with no human speech. These results exemplify the performance and versatility of different spectral subtraction algorithms. The category-specific results can be used to employ specific spectral subtraction algorithms for specific tasks for optimum performance.

Removal of impulse noise from an audio signal

The work examines the methods used to solve the given problem. Their architecture, principles of operation, specific examples in which they were used, their advantages and disadvantages were analyzed. The task of detecting and removing impulse noise in audio signals using machine learning methods is described. On the basis of the conducted research, a software solution using the spectral subtraction algorithm is proposed, which allows detecting and removing impulse noise in audio signals in order to improve the quality of audio perception. Figs: 3. Tabl.: 1. Refs: 10 titles.
 Keywords: audio signal; impulse noise; spectral subtraction; machine learning; recognition; noise removal.

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.

De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction

The generalized spectral subtraction algorithm (GBSS), which has extraordinary ability in background noise reduction, is historically one of the first approaches used for speech enhancement and dereverberation. However, the algorithm has not been applied to de-noise the room impulse response (RIR) to extend the reverberation decay range. The application of the GBSS algorithm in this study is stated as an optimization problem, that is, subtracting the noise level from the RIR while maintaining the signal quality. The optimization process conducted in the measurements of the RIRs with artificial noise and natural ambient noise aims to determine the optimal sets of factors to achieve the best noise reduction results regarding the largest dynamic range improvement. The optimal factors are set variables determined by the estimated SNRs of the RIRs filtered in the octave band. The acoustic parameters, the reverberation time (RT), and early decay time (EDT), and the dynamic range improvement of the energy decay curve were used as control measures and evaluation criteria to ensure the reliability of the algorithm. The de-noising results were compared with noise compensation methods. With the achieved optimal factors, the GBSS contributes to a significant effect in terms of dynamic range improvement and decreases the estimation errors in the RTs caused by noise levels.

Anti-Interference Test Technology of Transformer Noise

In order to enhance the transformer noise test accuracy, an anti- interference method based on wavelet packet decomposition and spectral subtraction algorithm is proposed in this paper. The tested original noise signal is decomposed with the wavelet packet method. The occurring time and frequency bands of the noisy signals are observed. In these bands, the noisy signals are further processed by spectral subtraction method. Finally, the pure transformer noise is obtained with wavelet packet reconstruction. The anti-interference effects of transformer noises in the conditions of various interference sources and signal to noise ratios are simulated. The result shows that the proposed method is effective in restraining the influence of diverse burst intermittent environmental noises on the transformer noise test result.

A Novel Method to Restrain Ambient Noise Interference in Transformer Noise Test

A preprocessing method based on wavelet packet decomposition and spectral subtraction algorithm is proposed in this paper. The tested original noise signal is decomposed with the wavelet packet analysis. It is divided into several bands with different frequency range. In these bands, the noisy signals are further processed by spectral subtraction method. The theoretical analysis result shows that the proposed method is effective in restraining the influence of surrounding noises on the result of transformer noise measurement. The proposed method can be referenced for the noise measurement of power transmission and transformation projects.

Interactions Between Digital Noise Reduction and Reverberation: Acoustic and Behavioral Effects.

Digital noise reduction (DNR) processing is used in hearing aids to enhance perception in noise by classifying and suppressing the noise acoustics. However, the efficacy of DNR processing is not known under reverberant conditions where the speech-in-noise acoustics are further degraded by reverberation. The purpose of this study was to investigate acoustic and perceptual effects of DNR processing across a range of reverberant conditions for individuals with hearing impairment. This study used an experimental design to investigate the effects of varying reverberation on speech-in-noise processed with DNR. Twenty-six listeners with mild-to-moderate sensorineural hearing impairment participated in the study. Speech stimuli were combined with unmodulated broadband noise at several signal-to-noise ratios (SNRs). A range of reverberant conditions with realistic parameters were simulated, as well as an anechoic control condition without reverberation. Reverberant speech-in-noise signals were processed using a spectral subtraction DNR simulation. Signals were acoustically analyzed using a phase inversion technique to quantify improvement in SNR as a result of DNR processing. Sentence intelligibility and subjective ratings of listening effort, speech naturalness, and background noise comfort were examined with and without DNR processing across the conditions. Improvement in SNR was greatest in the anechoic control condition and decreased as the ratio of direct to reverberant energy decreased. There was no significant effect of DNR processing on speech intelligibility in the anechoic control condition, but there was a significant decrease in speech intelligibility with DNR processing in all of the reverberant conditions. Subjectively, listeners reported greater listening effort and lower speech naturalness with DNR processing in some of the reverberant conditions. Listeners reported higher background noise comfort with DNR processing only in the anechoic control condition. Results suggest that reverberation affects DNR processing using a spectral subtraction algorithm in such a way that decreases the ability of DNR to reduce noise without distorting the speech acoustics. Overall, DNR processing may be most beneficial in environments with little reverberation and that the use of DNR processing in highly reverberant environments may actually produce adverse perceptual effects. Further research is warranted using commercial hearing aids in realistic reverberant environments.

Insight on the utilization of the noise estimation techniques in phase aware modified multiband spectral subtraction

ABSTRACTThe standard technique of all Spectral Subtraction algorithms that are used for Speech Enhancement (SE) is to find out the clean speech by subtracting the noise estimate from the noisy speech spectrum. So the essential criterion is the accuracy of noise estimation (NE) and it affects the potency of the SE. The traditional NE algorithms use voice activity detection (VAD) for noise estimation, which is troublesome at the cases of low SNR condition and when the noise characteristics are not stationary. The effect of noise is also different in different frequency bands of the noisy speech spectrum. In this paper, several NE techniques are implemented along with a phase aware and frequency dependent modified multi-band spectral subtraction. To give additional intelligibility to SE, the phase of the enhanced signal is modified using the phase spectrum compensation function. Finally, the mentioned methods are simulated and compared using several objective evaluation measures with the help of standard speech database.

Speech intelligibility enhancement for Thai-speaking cochlear implant listeners

Cochlear implant (CI) listeners encounter difficulties in communicating with other persons in noisy listening environments. However, most CI research has been carried out using the English language. In this study, single-channel speech enhancement (SE) strategies as a pre-processing approach for the CI system were investigated in terms of Thai speech intelligibility improvement. Two SE algorithms, namely multi-band spectral subtraction (MBSS) and Weiner filter (WF) algorithms, were evaluated. Speech signals consisting of monosyllabic and bisyllabic Thai words were degraded by speech-shaped noise and babble noise at SNR levels of 0, 5, and 10 dB. Then the noisy words were enhanced using SE algorithms. The enhanced words were fed into the CI system to synthesize vocoded speech. The vocoded speech was presented to twenty normal-hearing listeners. The results indicated that speech intelligibility was marginally improved by the MBSS algorithm and significantly improved by the WF algorithm in some conditions. The enhanced bisyllabic words showed a noticeably higher intelligibility improvement than the enhanced monosyllabic words in all conditions, particularly in speech-shaped noise. Such outcomes may be beneficial to Thai-speaking CI listeners.

Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process

Process monitoring is necessary in machining operation to increase productivity, improve surface quality, and reduce unscheduled downtime. Tool wear and breakage are important and common source of machining problems due to high temperatures and forces of the machining process. Therefore, it is highly beneficial to develop an online tool condition monitoring (TCM) system. This paper investigates a robust tool wear monitoring system for milling operation. Recent developments in machine learning, in particular deep learning methods, result in significant improvement in automation of different industries. Therefore, in this research, we employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation. Wavelet packet-based features are extracted for tool wear monitoring as a powerful time-frequency fault indicator. Moreover, a hybrid feature extraction method is proposed using wavelet time-frequency transformation and spectral subtraction algorithms to intensify the effect of tool wear in the signal and reduce the effect of other cutting parameters. CNN-based monitoring systems are compared with three other machine learning methods (support vector machine, Bayesian rigid network, and K nearest neighbor method) as the baseline. The research is validated using different datasets. The algorithms are implemented and compared using experimental force and vibration signals from LIPPS lab of ETS university as well as using current signals as the fault indicator from Nasa_Ames dataset.

Differentiation of key biomarkers in tea infusions using a target/nontarget gas chromatography/mass spectrometry workflow

Climatic conditions affect the chemical composition of edible crops, which can impact flavor, nutrition and overall consumer preferences. To understand these effects new data analysis software capable of tracking hundreds of compounds across years of samples under various environmental conditions is needed. Our recently developed mass spectral (MS) subtraction algorithms have been used with spectral deconvolution to efficiently analyze complex samples by 2-dimensional gas chromatography/mass spectrometry (GC-GC/MS). In this paper, we address the accuracy of identifying target and nontarget compounds by GC/MS. Findings indicate that Yunnan tea contains higher concentrations of floral compounds. In contrast, Fujian tea contains higher concentrations of compounds that exhibit fruity characteristics, but contains much less monoterpenes. Principal components analysis shows that seasonal changes in climate impact tea plants similarly despite location differences. For example, spring teas contained more of the sweet, floral and fruity compounds compared to summer teas, which had higher concentrations of green, woody, herbal compounds.

Pitch-Based Voice Activity Detection for Feedback Cancellation and Noise Reduction in Hearing Aids

In hearing aid (HA) systems, amplification of speech signals is done to compensate the hearing loss of patients. Background noise and feedback signals may also get amplified which degrade the intelligibility and quality of speech. To achieve high de-noise efficiency, signal processing unit in HA system has voice activity detector (VAD). The conventional VAD detects voice based on zero crossing rate or energy of input signals. However, these methods cannot perform well at low SNR or non-stationary noise environments. Since pitch is a special characteristic of speech and is basically independent of noise intensity, VAD based on pitch can have high accuracy even when the noise spectrum is changing drastically. In this paper, pitch-based VAD is presented and its accuracy is checked against zero crossing rate-based VAD (ZCR-VAD). For noise reduction, an improved multi-band spectral over-subtraction algorithm is employed along with the high accurate pitch-based VAD. For feedback cancellation, the performance of adaptive algorithms like NLMS, RLS and affine projection (AP) algorithms with pitch-based VAD is compared and it is observed that AP is suitable for feedback cancellation. The proposed noise reduction and feedback cancellation algorithm with pitch-based VAD method is tested with NOIZEUS speech database and with real-time noisy speech signals. The simulation results show that the accuracy of pitch-VAD is about 23% higher than that of ZCR-VAD. The SNR for the proposed noise reduction and feedback cancellation with pitch-VAD method is improved by 10 dB than conventional spectral subtraction and adaptive algorithms. Mean opinion score (MOS) obtained for the proposed method is 4.3 out of 5.

Robust Recognition of English Speech in Noisy Environments Using Frequency Warped Signal Processing

The performance level of speech recognizer drops significantly when there is an acoustic mismatch between training and operational environments. A speech recognizer is called robust if it preserves good recognition accuracy even in the mismatch conditions. Present study addresses the recognition of English speech in noisy environments and presents the comparative study of various frequency scales used in parameterization based on the average recognition rate. For the robust automatic speech reorganization, a front end signal enhancement component, spectral subtraction algorithm, is used to prefilter the noisy input speech prior fed to the recognizer. A number of frequency warped scales namely, perceptual scales viz, Mel scale, Bark scale, equivalent rectangular bandwidth rate scale, and a non-perceptual scale called uniform scale are used in the parameterization for feature extraction from enhanced speech. A suite of experiments is carried out to evaluate the performance of the speech recognizer, with and without the use of a front end signal enhancement component, in a variety of noisy environments. Recognition accuracy is tested in terms of word linguistic levels on a wide range of signal to noise ratios for both stationary and non-stationary noises.

An Improved Multi-band Spectral Subtraction using Mel-scale

In order to improve the intelligibility and quality of speech signal under different noise environments, an improved multi-band spectral subtraction algorithm using Mel-scale is proposed in this paper. In this algorithm, the whole speech spectrum is divided into N subbands without overlapping each other using the Mel frequency domain, and uses spectral subtraction to obtain the estimated pure speech independently in each subband. By using various types of noise at various SNRs, the experimental data, including SNR, PESQ measure and non-professional hearing test, are obtained. The result show that the algorithm is superior to the multi-band spectral subtraction with uniform segmentation and the traditional spectral subtraction algorithm, without causing considerable signal distortion and remnant musical noise.

Noise reduction using spectral-subtraction algorithm with over-subtraction and spectral-reservation factors adapted by harmonic properties

The power-spectral-subtraction (PSS) algorithm can remove interference noise efficiently by the subtraction of noise power from the power of a noise-interfered signal. However, the performance of this algorithm is not satisfactory for speech communication. This study proposes using an over-subtraction factor adapted by harmonic properties to increase the ability of noise removal. If the value of the over-subtraction factor is large enough, the interference noise can be removed efficiently; meanwhile, denoised speech suffers from serious speech distortion. On the contrary, plenty of residual noise exists when the value of the over-subtraction factor is too small, causing denoised speech to sound annoying to the human ear. How to define the value of this factor is critical to the quality of denoised speech. In addition, musical residual noise can be well reduced by using a spectral reservation factor. We employed the vowel harmonic properties to define the value of over-subtraction and reservation factors of the noisy spectra by using the sigmoid function. This function maps the relation between the values of over-subtraction and reservation factors, as well as the input SNRs. Experiments revealed that the proposed method can improve the performance of the PSS method significantly by increasing the reduction of interference noise and better preservation on weak vowels.

Pipelined Architecture of Multi-Band Spectral Subtraction Algorithm for Speech Enhancement

In this paper, a new pipelined architecture of the multi-band spectral subtraction algorithm has been proposed for real-time speech enhancement. The proposed hardware has been implemented on field programmable gate array (FPGA) device using Xilinx system generator (XSG), high-level programming tool, and Nexys-4 development board. The multi-band algorithm has been developed to reduce the additive colored noise that does not uniformly affect the entire frequency band of useful signal. All the algorithm steps have been successfully implemented on hardware. Pipelining has been employed on this hardware architecture to increase the data throughput. Speech enhancement performances obtained by the hardware architecture are compared to those obtained by MATLAB simulation using simulated and actual noises. The resource utilization, the maximum operating frequency, and power consumption are reported for a low-cost Artix-7 FPGA device.

Design A Dependent Voice Commands Selective System

Voice commands recognition is a way of understanding human speech and converting it to a communication input of computer. Based on the data used to feed the voice recognition system, systems could be classified as dependent or independent systems. In this paper, a dependent voice commands selective system is presented. The system uses the spectral subtraction algorithm for noise cancellation. A new algorithm is named Select Matching Command Algorithm SMCA is used for matching voice command identification. Six different users (3 male and 3 female) are used to test the system. Each user tests the system two times, one by using twenty four voice commands samples previously selected and other time by using another twenty four voice commands out of the twenty four preselected samples. The proposed system is able to correctly identify the matching voice commands 90.97% of the time when testing the system with the predefined voice commands. Testing the system with voice commands out of the predefined commands shows 92.36 % of no matching identification. According to the results noted from the six users, overall percent of selective accuracy of the suggested system is 91.67 %.