scholarly journals Speech Enhancement for Secure Communication Using Coupled Spectral Subtraction and Wiener Filter

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 897 ◽  
Author(s):  
Hilman Pardede ◽  
Kalamullah Ramli ◽  
Yohan Suryanto ◽  
Nur Hayati ◽  
Alfan Presekal
Keyword(s):  
Speech Enhancement ◽  
Communication System ◽  
Secure Communication ◽  
Wiener Filter ◽  
Speech Quality ◽  
Spectral Subtraction ◽  
Speech Signals ◽  
Voice Activity Detector ◽  
Noise Estimate

The encryption process for secure voice communication may degrade the speech quality when it is applied to the speech signals before encoding them through a conventional communication system such as GSM or radio trunking. This is because the encryption process usually includes a randomization of the speech signals, and hence, when the speech is decrypted, it may perceptibly be distorted, so satisfactory speech quality for communication is not achieved. To deal with this, we could apply a speech enhancement method to improve the quality of decrypted speech. However, many speech enhancement methods work by assuming noise is present all the time, so the voice activity detector (VAD) is applied to detect the non-speech period to update the noise estimate. Unfortunately, this assumption is not valid for the decrypted speech. Since the encryption process is applied only when speech is detected, distortions from the secure communication system are characteristically different. They exist when speech is present. Therefore, a noise estimator that is able to update noise even when speech is present is needed. However, most noise estimator techniques only adapt to slow changes of noise to avoid over-estimation of noise, making them unsuitable for this task. In this paper, we propose a speech enhancement technique to improve the quality of speech from secure communication. We use a combination of the Wiener filter and spectral subtraction for the noise estimator, so our method is better at tracking fast changes of noise without over-estimating them. Our experimental results on various communication channels indicate that our method is better than other popular noise estimators and speech enhancement methods.

Regression-Based Noise Modeling for Speech Signal Processing

2021 ◽  
pp. 2150022
Author(s):  
Caio Cesar Enside de Abreu ◽  
Marco Aparecido Queiroz Duarte ◽  
Bruno Rodrigues de Oliveira ◽  
Jozue Vieira Filho ◽  
Francisco Villarreal
Keyword(s):  
Speech Enhancement ◽  
Speech Processing ◽  
Acoustic Analysis ◽  
Voice Quality ◽  
Wiener Filter ◽  
Processing System ◽  
Speech Quality ◽  
Speech Signals ◽  
Speech Signal Processing ◽  
Acoustic Environment

Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presented and its effectiveness is tested in the speech enhancement context. For this purpose, partial least squares (PLS) regression is used to model the acoustic environment (AE) and a Wiener filter based on a priori SNR estimation is implemented to evaluate the proposed approach. Six noise types are used to create seven acoustically modeled noises. The basic idea is to consider the AE model to identify the noise type and estimate its power to be used in a speech processing system. Speech signals processed using the proposed method and classical noise estimators are evaluated through objective measures. Results show that the proposed method produces better speech quality than state-of-the-art noise estimators, enabling it to be used in real-time applications in the field of robotic, telecommunications and acoustic analysis.

scholarly journals Speech Enhancement based on 2D Gabor Filters for Arabic Phoneme Spoken by Malay Speakers

2018 ◽  
Vol 7 (4.11) ◽  
pp. 271
Author(s):  
Ali Abd Almisreb ◽  
Nooritawati Md Tahir ◽  
Ahmad Farid Abidin ◽  
Norashidah Md Din
Keyword(s):  
Speech Enhancement ◽  
Gabor Filter ◽  
Wiener Filter ◽  
Gabor Filters ◽  
Speech Signals ◽  
Suggested Approach ◽  
Arabic Speakers ◽  
Native Arabic Speakers ◽  
Enhancement Method ◽  
2D Gabor Filter

In this paper, a speech enhancement method using 2D Gabor filter is proposed. The proposed filter is used to enhance Arabic phoneme speech signals that have been recorded under control environment namely indoor room recording. All the phoneme signals are spoken by Malay speakers and considered as non-native Arabic speakers. Firstly, corrupted speech signals by noise must be enhanced before further processing. The effectiveness of the suggested approach is evaluated in compare with Wiener filter. It is proven that the proposed 2D Gabor filters performed appropriately for speech enhancement purpose at different wavelengths.  

scholarly journals Application of Perceptual Filtering Models to Noisy Speech Signals Enhancement

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Novlene Zoghlami ◽  
Zied Lachiri
Keyword(s):  
Speech Enhancement ◽  
Noise Suppression ◽  
Filter Banks ◽  
Perceptual Quality ◽  
Speech Signals ◽  
Subjective Test ◽  
Noisy Speech ◽  
Estimation Algorithms ◽  
Noise Estimate ◽  
Quality Rating

This paper describes a new speech enhancement approach using perceptually based noise reduction. The proposed approach is based on the application of two perceptual filtering models to noisy speech signals: the gammatone and the gammachirp filter banks with nonlinear resolution according to the equivalent rectangular bandwidth (ERB) scale. The perceptual filtering gives a number of subbands that are individually spectral weighted and modified according to two different noise suppression rules. The importance of an accurate noise estimate is related to the reduction of the musical noise artifacts in the processed speech that appears after classic subtractive process. In this context, we use continuous noise estimation algorithms. The performance of the proposed approach is evaluated on speech signals corrupted by real-world noises. Using objective tests based on the perceptual quality PESQ score and the quality rating of signal distortion (SIG), noise distortion (BAK) and overall quality (OVRL), and subjective test based on the quality rating of automatic speech recognition (ASR), we demonstrate that our speech enhancement approach using filter banks modeling the human auditory system outperforms the conventional spectral modification algorithms to improve quality and intelligibility of the enhanced speech signal.

scholarly journals Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing

2017 ◽  
Vol 7 (4) ◽  
pp. 1941
Author(s):  
Amart Sulong ◽  
Teddy Surya Gunawan ◽  
Othman O Khalifa ◽  
Mira Kartiwi ◽  
Hassan Dao
Keyword(s):  
Compressive Sensing ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Single Channel ◽  
Objective Assessment ◽  
Amplitude Spectrum ◽  
Wiener Filter ◽  
Speech Quality ◽  
Interactive Effects ◽  
Assessment Tests

<table width="593" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p class="Text">The speech enhancement algorithms are utilized to overcome multiple limitation factors in recent applications such as mobile phone and communication channel. The challenges focus on corrupted speech solution between noise reduction and signal distortion. We used a modified Wiener filter and compressive sensing (CS) to investigate and evaluate the improvement of speech quality. This new method adapted noise estimation and Wiener filter gain function in which to increase weight amplitude spectrum and improve mitigation of interested signals. The CS is then applied using the gradient projection for sparse reconstruction (GPSR) technique as a study system to empirically investigate the interactive effects of the corrupted noise and obtain better perceptual improvement aspects to listener fatigue with noiseless reduction conditions. The proposed algorithm shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at various noise type conditions of 0, 5, 10, 15 dB SNRs. Therefore, the proposed algorithm significantly achieved the speech quality improvement and efficiently obtained higher performance resulting in better noise reduction compare to other conventional algorithms. </p></td></tr></tbody></table>

scholarly journals Assessment of Speech Quality during Speech Rehabilitation Based on the Solution of the Classification Problem

2021 ◽  
Author(s):  
Evgeny Kostyuchenko ◽  
Ivan Rakhmanenko ◽  
Alexander Shelupanov ◽  
Lidiya Balatskaya ◽  
Ivan Sidorov
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Speech Quality ◽  
Speech Signals ◽  
Reference Class ◽  
Experimental Assessment ◽  
Speech Rehabilitation ◽  
Expert Assessments

The article considers an approach to the problem of assessing the quality of speech during speech rehabilitation as a classification problem. For this, a classifier is built on the basis of an LSTM neural network for dividing speech signals into two classes: before the operation and immediately after. At the same time, speech before the operation is the standard to which it is necessary to approach in the process of rehabilitation. The metric of belonging of the evaluated signal to the reference class acts as an assessment of speech. An experimental assessment of rehabilitation sessions and a comparison of the resulting assessments with expert assessments of phrasal intelligibility were carried out.

Performance evaluation on multi-channel Wiener filter based speech enhancement for unmanned aerial vehicles recordings

2021 ◽  
Vol 263 (3) ◽  
pp. 3584-3594
Author(s):  
Yameizhen Li ◽  
Benjamin Yen ◽  
Yusuke Hioka
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Speech Enhancement ◽  
Wiener Filter ◽  
Speech Distortion ◽  
Aerial Vehicles ◽  
Audio Recordings ◽  
Aerial Vehicle ◽  
Noise Contamination

Recording speech from unmanned aerial vehicles has been attracting interest due to its broad application including filming, search and rescue, and surveillance. One of the challenges in this problem is the quality of the speech recorded due to contamination by various interfering noise. In particular, noise contamination due to those radiated by the unmanned aerial vehicles rotors significantly impacts the overall quality of the audio recordings. Multi-channel Wiener filter has been a commonly used technique for speech enhancement because of its robustness under practical setup. Existing studies have also utilised such techniques in speech enhancement for unmanned aerial vehicle recordings, such as the well-known beamformer with postfiltering framework. However, many variants of the multi-channel Wiener filter have also been developed over recent years such as the speech distortion weighted multi-channel Wiener filter. To address these recent advancements, in this study we compare the performance of these variants of techniques. In particular, we explore the benefits these techniques may bring forth in the setting of audio recordings from an unmanned aerial vehicle.

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