Speech Enhancement Techniques Research Articles

New research on monaural speech segregation based on quality assessment

Speech enhancement (SE) is a pivotal technology in enhancing the quality and intelligibility of speech signals. Nevertheless, when processing speech signals under conditions of high signal-to-noise ratio (SNR), conventional SE techniques may inadvertently lead to a diminution in the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). This article introduces the innovative incorporation of the Non-Intrusive Speech Quality Assessment (NISQA) algorithm into SE systems. Through the comparison of pre and post-enhancement speech quality scores, it discerns whether the speech signal under consideration warrants enhancement processing, thereby mitigating potential deterioration in PESQ and STOI. Furthermore, this study delves into the ramifications of five prevalent speech features, namely, Mel Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), Relative Spectral Trans-formed Perceptual Linear Prediction coefficients (RASTA-PLP), Amplitude Modulation Spectrogram (AMS), and Multi-Resolution Cochleagram (MRCG), on PESQ and STOI under varying noise conditions. Experimental outcomes underscore that MRCG consistently emerges as the optimal and most stable feature for STOI, while the feature yielding the highest PESQ score exhibits intricate correlations with the background noise type, SNR level, and noise compatibility with the speech signal. Consequently, we propose an SE methodology founded on quality assessment and feature selection, facilitating the adaptive selection of optimal features tailored to distinct background noise scenarios, thereby always maintain the highest caliber enhancement effect with regard to PESQ metrics.

Comparative Studies of Single-Channel Speech Enhancement Techniques

Several speech enhancement techniques like Spectral Subtraction, MMSE, Log-MMSE, -order MMSE, adaptive -order MMSE and compressive sensing methods are developed worldwide. Scientists, engineers and researchers have implemented, evaluated and tested all methods individually with the different speech corpus. However, we found few articles on comparative studies of various speech enhancement techniques. In the present paper, several speech enhancement techniques have been studied, and their performance in terms of speech quality measures is compared objectively and subjectively. The results have been evaluated not only through speech quality measures but also in terms of waveform and spectrogram for speech enhancement applications. For this, MATLAB is used for the simulation of all methods. After getting the enhanced speech signals, we evaluated their enhanced speech signals of the methods. Results in terms of objective evaluation parameters indicated that the adaptive -order MMSE-based method produces good-quality speech signals compared to the other methods. Also, we evaluated their enhanced speech signals using a listening test, i.e. subjective evaluation. In the subjective quality test through mean opinion score (using the listening test), the performance of the adaptive -order MMSE method and GOMP are equal. In the case of waveform and spectrogram, the visualisation of enhanced speech signal obtained from GOMP-based compressive algorithms is very close to clean speech signal.

Deep neural network techniques for monaural speech enhancement and separation: state of the art analysis

AbstractDeep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in tasks such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain. More specifically, DNN models have been applied in speech enhancement and separation to perform speech denoising, dereverberation, speaker extraction and speaker separation. In this paper, we review the current DNN techniques being employed to achieve speech enhancement and separation. The review looks at the whole pipeline of speech enhancement and separation techniques from feature extraction, how DNN-based tools models both global and local features of speech, model training (supervised and unsupervised) to how they address label ambiguity problem. The review also covers the use of domain adaptation techniques and pre-trained models to boost speech enhancement process. By this, we hope to provide an all inclusive reference of all the state of art DNN based techniques being applied in the domain of speech separation and enhancement. We further discuss future research directions. This survey can be used by both academic researchers and industry practitioners working in speech separation and enhancement domain.

ADVANCING GASIFICATION-COMBINED UP AND DOWN DRAFT GASIFIER-BASED TREATMENT OF TEXTILE WASTE: ASSESSING FEASIBILITY, ENVIRONMENTAL IMPACTS AND ENERGY RECOVERY POTENTIAL

Speech enhancement, a critical component in various applications including voice assistants and telecommunication systems, aims to improve the quality and intelligibility of spoken content. This paper introduces a novel approach leveraging Natural Language Processing (NLP) techniques for speech enhancement. By harnessing the power of NLP models, we propose a methodology that not only considers the acoustic features but also incorporates linguistic context in the enhancement process. The study encompasses data collection, preprocessing, and the application of advanced NLP algorithms to extract relevant linguistic information. The experimental results demonstrate a significant improvement in speech quality, as evidenced by a substantial increase in Signal-to-Noise Ratio (SNR) and Mean Opinion Score (MOS) compared to conventional methods. Furthermore, a comparative analysis with existing techniques showcases the superiority of our approach. This research not only contributes to the advancement of speech enhancement technology but also underscores the pivotal role of NLP in this domain. The findings pave the way for future research directions, suggesting potential applications in areas such as voice recognition systems and real-time communication platforms. Keywords:Acoustic Features, Linguistic Context, Mean Opinion Score (MOS), Natural Language Processing, NLP Algorithms, Signal-to-Noise Ratio (SNR), Speech Enhancement, Speech Quality, Telecommunication Systems, Voice Assistants.

Orthogonalization of the Sensing Matrix Through Dominant Columns in Compressive Sensing for Speech Enhancement

This paper introduces a novel speech enhancement approach called dominant columns group orthogonalization of the sensing matrix (DCGOSM) in compressive sensing (CS). DCGOSM optimizes the sensing matrix using particle swarm optimization (PSO), ensuring separate basis vectors for speech and noise signals. By utilizing an orthogonal matching pursuit (OMP) based CS signal reconstruction with this optimized matrix, noise components are effectively avoided, resulting in lower noise in the reconstructed signal. The reconstruction process is accelerated by iterating only through the known speech-contributing columns. DCGOSM is evaluated against various noise types using speech quality measures such as SNR, SSNR, STOI, and PESQ. Compared to other OMP-based CS algorithms and deep neural network (DNN)-based speech enhancement techniques, DCGOSM demonstrates significant improvements, with maximum enhancements of 42.54%, 62.97%, 27.48%, and 8.72% for SNR, SSNR, PESQ, and STOI, respectively. Additionally, DCGOSM outperforms DNN-based techniques by 20.32% for PESQ and 8.29% for STOI. Furthermore, it reduces recovery time by at least 13.2% compared to other OMP-based CS algorithms.

Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique

The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of sifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.

A Post-Filtering for Performance of Differential Microphone Array

Microphone array (MA) is a perspective technique for speech enhancement, because of its simplicity and lowcomputation. MA can be decomposed into a concatenation of a spatial filtering, which provides beampattern toward a certain target directional speech source while attenuating noise, interference or third-party speaker from other directions, and a post-filtering, which suppresses the remaining noise at the final output signal. Differential Microphone Array (DIF) is one basic MA, which has many advantages for extracting the desired speech and null-steering to direction of noise. Nevertheless, in real situation, the performance of DIF often deteriorated due to phase error, imprecise the direction-ofarrival (DOA) of interest signal, microphone mismatches or imperfect distribution of MA, which significantly degrade the performance and the speech quality of DIF. In this article, the author proposed a Post-Filtering (PF), which based on the properties of considered recording environment DOA to enhance the speech quality of target speaker and reduce the other non-target speaker at the opposite direction. Experimental results were verified in real scenario with dialogue between two speakers, which stand opposite direction, and show the effectiveness of the suggested PF when compared with the author’s previous work.

MASTER: Microphone Array Source Time Difference Eco Canceller via Reconstructed Spiking Neural Network

In a real-time environment, background noise frequently makes it harder for listeners to understand speech. Several speech enhancement algorithms based on supervised learning have been applied to improve the clarity and quality of speech. However, neural network-based speech enhancement techniques focus on the magnitude spectrogram while ignoring the phase misalignment between the noisy and clean voice samples. The primary goal of the proposed method is to boost learning language, eliminate echo, and enhance audio quality. In this paper Microphone sensor Array Source Time Difference, Eco canceller via Reconstructed Spiking neural network (MASTER) has been proposed. Initially, the input signals are pre-processed using Hanning Windowing & second-order Butterworth bandpass Filter. The direction of the preprocessed input signal is calculated based on the time delay of the source received between the microphone sensor pairs. The Direction of Arrival (DOA) of the input source is fed into the Reconstructed spiking Convolutional Neural network (RSpiCN Net) that consists of three modules namely spike generation, Masking, and reconstruction for classifying the signals into desired and interference sources. The interference source is fed into an Adaptive Least Mean Square (ALMS) Filter algorithm to get the enhanced signal from the selected source. The proposed method yields better SNR of 7.2 dB, SDR of 6.2 dB, SIR of 8 dB, and PESQ of 2.6 compared to the existing method. The SDR of the proposed method is 3.5%, 4.56%, and 7.85% better than the existing FLANN, R-CED, and Cycle GAN respectively.

Characterization of Deep Learning-Based Speech-Enhancement Techniques in Online Audio Processing Applications.

Deep learning-based speech-enhancement techniques have recently been an area of growing interest, since their impressive performance can potentially benefit a wide variety of digital voice communication systems. However, such performance has been evaluated mostly in offline audio-processing scenarios (i.e., feeding the model, in one go, a complete audio recording, which may extend several seconds). It is of significant interest to evaluate and characterize the current state-of-the-art in applications that process audio online (i.e., feeding the model a sequence of segments of audio data, concatenating the results at the output end). Although evaluations and comparisons between speech-enhancement techniques have been carried out before, as far as the author knows, the work presented here is the first that evaluates the performance of such techniques in relation to their online applicability. This means that this work measures how the output signal-to-interference ratio (as a separation metric), the response time, and memory usage (as online metrics) are impacted by the input length (the size of audio segments), in addition to the amount of noise, amount and number of interferences, and amount of reverberation. Three popular models were evaluated, given their availability on public repositories and online viability, MetricGAN+, Spectral Feature Mapping with Mimic Loss, and Demucs-Denoiser. The characterization was carried out using a systematic evaluation protocol based on the Speechbrain framework. Several intuitions are presented and discussed, and some recommendations for future work are proposed.

Wavelet-Based Weighted Low-Rank Sparse Decomposition Model for Speech Enhancement Using Gammatone Filter Bank Under Low SNR Conditions

Estimating noise-related parameters in unsupervised speech enhancement (SE) techniques is challenging in low SNR and non-stationary noise environments. In the recent SE approaches, the best results are achieved by partitioning noisy speech spectrograms into low-rank noise and sparse speech parts. However, a few limitations reduce the performance of these SE methods due to the use of overlap and add in STFT process, noisy phase, due to inaccurate estimation of low rank in nuclear norm minimization and Euclidian distance measure in the cost function. These aspects can cause a loss of information in the reconstructed signal when compared to clean speech. To solve this, we propose a novel wavelet-based weighted low-rank sparse decomposition model for enhancing speech by incorporating a gammatone filter bank and Kullback–Leibler divergence. The proposed framework differs from other strategies in which the SE is carried entirely in time domain without the need for noise estimation. Further, to reduce the word error rate, these algorithms were trained and tested on a typical automatic speech recognition module. The experimental findings indicate that the proposed cascaded model has shown significant improvement under low SNR conditions over individual and traditional methods with regard to SDR, PESQ, STOI, SIG, BAK and OVL.

An Analysis of Traditional Noise Power Spectral Density Estimators Based on the Gaussian Stochastic Volatility Model

Many single- and multi-channel speech enhancement techniques, old and new, rely in one way or another on estimates of the noise power spectral density (PSD). For example, the classical Wiener filter requires that either the speech or noise PSD be estimated. Typically, the noise PSD is estimated, as it is often easier to model and estimate than the speech. As a result, much attention has been paid to this important problem over the past couple of decades, with important scientific milestones being the minimum statistics (MS), the minima controlled recursive averaging (IMCRA), and the minimum mean squared (MMSE) estimators. Despite leading to major progress, these estimators are rather ad hoc, making them difficult to tune and improve in a systematic manner. In this article, we analyse some of the common heuristics employed in such noise PSD estimators to put them on firmer mathematical ground. More specifically, we use the Gaussian stochastic volatility model and show that the MMSE noise PSD estimator can be interpreted as a special case thereof. Moreover, we analyze the related problem of speech presence probability (SPP) estimation and show that the SPP estimation performed in the MMSE noise PSD estimator can be interpreted as an SNR estimator in the context of the Gaussian stochastic volatility model.

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

Comparison of enhancement techniques based on neural networks for attenuated voice signal captured by flexible vibration sensors on throats

Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech. However, high-frequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction. In this paper, speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared. Four kinds of speech enhancement algorithms based on a fully connected neural network (FCNN), a long short-term memory (LSTM), a bidirectional long short-term memory (BLSTM), and a convolutional-recurrent neural network (CRNN) are adopted to enhance the sensor signals, and their performance after deployment on four kinds of edge and cloud platforms is also investigated. Experimental results show that the BLSTM performs best in improving speech quality, but is poorest with regard to hardware deployment. It improves short-time objective intelligibility (STOI) by 0.18 to nearly 0.80, which corresponds to a good intelligibility level, but it introduces latency as well as being a large model. The CRNN, which improves STOI to about 0.75, ranks second among the four neural networks. It is also the only model that is able to achieves real-time processing with all four hardware platforms, demonstrating its great potential for deployment on mobile platforms. To the best of our knowledge, this is one of the first trials to systematically and specifically develop processing techniques for bone-conduction speed signals captured by flexible sensors. The results demonstrate the possibility of realizing a wearable lightweight speech collection system based on flexible vibration sensors and real-time speech enhancement to compensate for high-frequency attenuation.

A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

This paper presents a novel self-adaptive approach for speech enhancement in the context of highly nonstationary noise. A two-stage deep neuroevolutionary technique for speech enhancement is proposed. The first stage is composed of a deep neural network (DNN) method for speech enhancement. Two DNN methods were tested at this stage, namely, both a deep complex convolution recurrent network (DCCRN) and a residual long short-term memory neural network (ResLSTM). The ResLSTM method was combined with a minimum mean-square error method to perform a preliminary enhancement. The ResLSTM network is used as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> signal-to-noise ratio (SNR) estimator. The second stage implements a self-adaptive multiband spectral subtraction enhancement method using tuning optimization based on a genetic algorithm. The proposed two-stage technique is evaluated using objective measures of speech quality and intelligibility. The experiments are carried out using the NOIZEUS noisy speech corpus using conditions of real-world stationary, colored, and nonstationary noise sources at multiple SNR levels. These experiments demonstrate the advantage of building a cooperative approach using evolutionary and deep learning-based techniques that are capable of achieving robust speech enhancement in adverse conditions. Indeed, the experimental tests show that the proposed two-stage technique outperformed a baseline implementation using a state-of-the-art deep learning approach by an average 13% and 6% improvement for six noise conditions at a −5 dB and a 0 dB input SNR, respectively.

A New Speech Enhancement Technique Based on Stationary Bionic Wavelet Transform and MMSE Estimate of Spectral Amplitude

Speech enhancement has gained considerable attention in the employment of speech transmission via the communication channel, speaker identification, speech-based biometric systems, video conference, hearing aids, mobile phones, voice conversion, microphones, and so on. The background noise processing is needed for designing a successful speech enhancement system. In this work, a new speech enhancement technique based on Stationary Bionic Wavelet Transform (SBWT) and Minimum Mean Square Error (MMSE) Estimate of Spectral Amplitude is proposed. This technique consists at the first step in applying the SBWT to the noisy speech signal, in order to obtain eight noisy wavelet coefficients. The denoising of each of those coefficients is performed through the application of the denoising method based on MMSE Estimate of Spectral Amplitude. The SBWT inverse, S B W T − 1 , is applied to the obtained denoised stationary wavelet coefficients for finally obtaining the enhanced speech signal. The proposed technique’s performance is proved by the calculation of the Signal to Noise Ratio (SNR), the Segmental SNR (SSNR), and the Perceptual Evaluation of Speech Quality (PESQ).

A variant of SWEMDH technique based on variational mode decomposition for speech enhancement

In Speech Enhancement (SE) techniques, the major challenging task is to suppress non-stationary noises including white noise in real-time application scenarios. Many techniques have been developed for enhancing the vocal signals; however, those were not effective for suppressing non-stationary noises very well. Also, those have high time and resource consumption. As a result, Sliding Window Empirical Mode Decomposition and Hurst (SWEMDH)-based SE method where the speech signal was decomposed into Intrinsic Mode Functions (IMFs) based on the sliding window and the noise factor in each IMF was chosen based on the Hurst exponent data. Also, the least corrupted IMFs were utilized to restore the vocal signal. However, this technique was not suitable for white noise scenarios. Therefore in this paper, a Variant of Variational Mode Decomposition (VVMD) with SWEMDH technique is proposed to reduce the complexity in real-time applications. The key objective of this proposed SWEMD-VVMDH technique is to decide the IMFs based on Hurst exponent and then apply the VVMD technique to suppress both low- and high-frequency noisy factors from the vocal signals. Originally, the noisy vocal signal is decomposed into many IMFs using SWEMDH technique. Then, Hurst exponent is computed to decide the IMFs with low-frequency noisy factors and Narrow-Band Components (NBC) is computed to decide the IMFs with high-frequency noisy factors. Moreover, VVMD is applied on the addition of all chosen IMF to remove both low- and high-frequency noisy factors. Thus, the speech signal quality is improved under non-stationary noises including additive white Gaussian noise. Finally, the experimental outcomes demonstrate the significant speech signal improvement under both non-stationary and white noise surroundings.

A Hybrid Speech Enhancement Algorithm for Voice Assistance Application.

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

Real-time joint dereverberation and speech enhancement for hearing aid applications using edge devices

In this work, we propose a joint dereverberation and speech enhancement technique for real-time applications. Speech dereverberation is based on the coherent-to-diffuse energy ratio (CDR) measured from the direction-of-arrival (DOA) dependent complex spatial coherence function. A personalized speech enhancement (SE) technique based on joint maximum a posteriori probability (JMAP) estimation that inherently accounts for the effects of changes in acoustic path and reverberation is used. The combination of these masking functions is used to enhance the speech corrupted by reverberation and noise in a frame-by-frame process. The proposed method is implemented on the smartphone (edge device), to illustrate real-time usability. The efficiency of the proposed method is evaluated using speech quality and intelligibility measures and compared with that of other benchmark techniques.

Phase sensitive masking-based single channel speech enhancement using conditional generative adversarial network

We propose PSMGAN, an efficient phase sensitive masking-based single-channel speech enhancement technique using a conditional generative adversarial network (cGAN). The time–frequency (T-F) masking-based speech enhancement approaches through deep neural networks (DNNs) have shown large speech intelligibility improvements. However, these approaches fail to achieve better enhancement results at low signal-to-noise ratio (SNR) conditions since they ignore the phase information during reconstruction. Alternatively, GANs have been introduced effectively for speech enhancement and achieved improved performance due to the adversarial training. Motivated by the recent success of GAN, we introduce the phase sensitive masking (PSM) in a cGAN framework for speech enhancement task. The reason for choosing a conditional generative model is that the data generation process can be controlled with the use of additional temporal context information. In addition, we use gradient penalty regularization in the discriminator of the cGAN network to avoid vanishing gradients problem which in turn stabilizes the training of the cGAN network and increases the quality of the generated samples. The use of PSM is due to the fact that it involves both amplitude and phase information and produces an improved estimate of clean speech signal with higher SNR as compared to other T-F masks. Experimental results show the proposed PSM based cGAN architecture has shown significant improvements in performance measures compared to other baselines such as SEGAN, Deep Feature Loss, MetricGAN, AECNN, DNN-cIRM, and end-to-end approach with reference to quality and intelligibility.

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

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.