Fundamentals, present and future perspectives of speech enhancement

Das, Nabanita; Chakraborty, Sayan; Chaki, Jyotismita; Padhy, Neelamadhab; Dey, Nilanjan

doi:10.1007/s10772-020-09674-2

Cited by 57 publications

(25 citation statements)

References 141 publications

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“…The most cited methods are the Cepstral Mean Subtraction (CMS) [18], the Power-Normalized Cepstral Coefficients (PNCCs) [19], and the Cepstral Mean Normalization (CMN) [20] which is a popular feature compensation method dealing with convolutional noise. In this same context, the majority of the published works demonstrated that the wavelet-based feature extraction [21][22][23][24] has better performance improvement than traditional Cepstral features in noisy environments. The already presented wavelet-based techniques rely on the multiresolution PWP properties and combine the extracted MFCC features from various frequency sub bands to a unique feature vector.…”

Section: Introductionmentioning

confidence: 97%

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Helali

Hajaiej

Chérif

2020

Eng. Technol. Appl. Sci. Res.

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The real-time performance of Automatic Speech Recognition (ASR) is a big challenge and needs high computing capability and exhaustive memory consumption. Getting a robust performance against inevitable various difficult situations such as speaker variations, accents, and noise is a tedious task. It’s crucial to expand new and efficient approaches for speech signal extraction features and pre-processing. In order to fix the high dependency issue related to processing succeeding steps in ARS and enhance the extracted features’ quality, noise robustness can be solved within the ARS extraction block feature, removing implicitly the need for further additional specific compensation parameters or data collection. This paper proposes a new robust acoustic extraction approach development based on a hybrid technique consisting of Perceptual Wavelet Packet (PWP) and Mel Frequency Cepstral Coefficients (MFCCs). The proposed system was implemented on a Rasberry Pi board and its performance was checked in a clean environment, reaching 99% average accuracy. The recognition rate was improved (from 80% to 99%) for the majority of Signal-to-Noise Ratios (SNRs) under real noisy conditions for positive SNRs and considerably improved results especially for negative SNRs.

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Section: Introductionmentioning

confidence: 97%

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Helali

Hajaiej

Chérif

2020

Eng. Technol. Appl. Sci. Res.

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“…Undoubtedly, the popularity of DL in image processing, computer vision, and natural language processing has led to significant impact in fields closely related to spatial audio, including speech enhancement or music information retrieval [15,16]. While ML algorithms have already positioned themselves at the top of the state of the art within the aforementioned fields, their use in immersive spatial audio is only emerging, as it will be illustrated throughout this review.…”

Section: Introductionmentioning

confidence: 99%

An overview of machine learning and other data-based methods for spatial audio capture, processing, and reproduction

Cobos

Ahrens

Kowalczyk

et al. 2022

J AUDIO SPEECH MUSIC PROC.

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The domain of spatial audio comprises methods for capturing, processing, and reproducing audio content that contains spatial information. Data-based methods are those that operate directly on the spatial information carried by audio signals. This is in contrast to model-based methods, which impose spatial information from, for example, metadata like the intended position of a source onto signals that are otherwise free of spatial information. Signal processing has traditionally been at the core of spatial audio systems, and it continues to play a very important role. The irruption of deep learning in many closely related fields has put the focus on the potential of learning-based approaches for the development of data-based spatial audio applications. This article reviews the most important application domains of data-based spatial audio including well-established methods that employ conventional signal processing while paying special attention to the most recent achievements that make use of machine learning. Our review is organized based on the topology of the spatial audio pipeline that consist in capture, processing/manipulation, and reproduction. The literature on the three stages of the pipeline is discussed, as well as on the spatial audio representations that are used to transmit the content between them, highlighting the key references and elaborating on the underlying concepts. We reflect on the literature based on a juxtaposition of the prerequisites that made machine learning successful in domains other than spatial audio with those that are found in the domain of spatial audio as of today. Based on this, we identify routes that may facilitate future advancement.

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“…Nevertheless, the vast majority of these apps are not resilient when dealing with interference. As a result, speech enhancement (SE) [ 1 , 2 , 3 , 4 ], a technique that tries to enhance the intelligibility and quality of the original speech signals, has seen widespread use in the context of these applications. Over the last few years, deep learning techniques have seen an increased amount of use when it comes to the construction of SE systems.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep Convolutional Recurrent Models for Noise Robust Waveform Speech Enhancement

Ullah

Wuttisittikulkij

Chaudhary

et al. 2022

Sensors

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Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

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Fundamentals, present and future perspectives of speech enhancement

Cited by 57 publications

References 141 publications

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

An overview of machine learning and other data-based methods for spatial audio capture, processing, and reproduction

End-to-End Deep Convolutional Recurrent Models for Noise Robust Waveform Speech Enhancement

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