Non-intrusive speech quality assessment using context-aware neural networks

To meet the human perceived quality of experience (QoE) while communicating over various Voice over Internet protocol (VoIP) applications, for example Google Meet, Microsoft Skype, Apple FaceTime, etc. a precise speech quality assessment metric is needed. The metric should be able to detect and segregate different types of noise degradations present in the surroundings before measuring and monitoring the quality of speech in real-time. Our research is motivated by the lack of clear evidence presenting speech quality metric that can firstly distinguish different types of noise degradations before providing speech quality prediction decision. To that end, this paper presents a novel non-intrusive speech quality assessment metric using context-aware neural networks in which the noise class (context) of the degraded or noisy speech signal is first identified using a classifier then deep neutral networks (DNNs) based speech quality metrics (SQMs) are trained and optimized for each noise class to obtain the noise class-specific (context-specific) optimized speech quality predictions (MOS scores). The noisy speech signals, that is, clean speech signals degraded by different types of background noises are taken from the NOIZEUS speech corpus. Results demonstrate that even in the presence of less number of speech samples available from the NOIZEUS speech corpus, the proposed metric outperforms in different contexts compared to the metric where the contexts are not classified before speech quality prediction.publishedVersio

Speech quality prediction for voice over Internet protocol networks

Merged with duplicate record 10026.1/878 on 03.01.2017 by CS (TIS). Merged with duplicate record 10026.1/1657 on 15.03.2017 by CS (TIS)This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.IP networks are on a steep slope of innovation that will make them the long-term carrier of all types of traffic, including voice. However, such networks are not designed to support real-time voice communication because their variable characteristics (e.g. due to delay, delay variation and packet loss) lead to a deterioration in voice quality. A major challenge in such networks is how to measure or predict voice quality accurately and efficiently for QoS monitoring and/or control purposes to ensure that technical and commercial requirements are met. Voice quality can be measured using either subjective or objective methods. Subjective measurement (e.g. MOS) is the benchmark for objective methods, but it is slow, time consuming and expensive. Objective measurement can be intrusive or non-intrusive. Intrusive methods (e.g. ITU PESQ) are more accurate, but normally are unsuitable for monitoring live traffic because of the need for a reference data and to utilise the network. This makes non-intrusive methods(e.g. ITU E-model) more attractive for monitoring voice quality from IP network impairments. However, current non-intrusive methods rely on subjective tests to derive model parameters and as a result are limited and do not meet new and emerging applications. The main goal of the project is to develop novel and efficient models for non-intrusive speech quality prediction to overcome the disadvantages of current subjective-based methods and to demonstrate their usefulness in new and emerging VoIP applications. The main contributions of the thesis are fourfold: (1) a detailed understanding of the relationships between voice quality, IP network impairments (e.g. packet loss, jitter and delay) and relevant parameters associated with speech (e.g. codec type, gender and language) is provided. An understanding of the perceptual effects of these key parameters on voice quality is important as it provides a basis for the development of non-intrusive voice quality prediction models. A fundamental investigation of the impact of the parameters on perceived voice quality was carried out using the latest ITU algorithm for perceptual evaluation of speech quality, PESQ, and by exploiting the ITU E-model to obtain an objective measure of voice quality. (2) a new methodology to predict voice quality non-intrusively was developed. The method exploits the intrusive algorithm, PESQ, and a combined PESQ/E-model structure to provide a perceptually accurate prediction of both listening and conversational voice quality non-intrusively. This avoids time-consuming subjective tests and so removes one of the major obstacles in the development of models for voice quality prediction. The method is generic and as such has wide applicability in multimedia applications. Efficient regression-based models and robust artificial neural network-based learning models were developed for predicting voice quality non-intrusively for VoIP applications. (3) three applications of the new models were investigated: voice quality monitoring/prediction for real Internet VoIP traces, perceived quality driven playout buffer optimization and perceived quality driven QoS control. The neural network and regression models were both used to predict voice quality for real Internet VoIP traces based on international links. A new adaptive playout buffer and a perceptual optimization playout buffer algorithms are presented. A QoS control scheme that combines the strengths of rate-adaptive and priority marking control schemes to provide a superior QoS control in terms of measured perceived voice quality is also provided. (4) a new methodology for Internet-based subjective speech quality measurement which allows rapid assessment of voice quality for VoIP applications is proposed and assessed using both objective and traditional MOS test methods

DNN-Based Source Enhancement to Increase Objective Sound Quality Assessment Score

We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been used as a mapping function to estimate time-frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error (MSE). Since OSQA scores have been used widely for soundquality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create highquality output signals. However, since most OSQA scores are not analytically tractable, i.e., they are black boxes, the gradient of the objective function cannot be calculated by simply applying back-propagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of black-box optimization, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability-density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized

Non-intrusive method for audio quality assessment of lossy-compressed music recordings using convolutional neural networks

Most of the existing algorithms for the objective audio quality assessment are intrusive, as they require access both to an unimpaired reference recording and an evaluated signal. This feature excludes them from many practical applications. In this paper, we introduce a non-intrusive audio quality assessment method. The proposed method is intended to account for audio artefacts arising from the lossy compression of music signals. During its development, 250 high-quality uncompressed music recordings were collated. They were subsequently processed using the selection of five popular audio codecs, resulting in the repository of 13,000 audio excerpts representing various levels of audio quality. The proposed non-intrusive method was trained with the data obtained employing a well-established intrusive model (ViSQOL v3). Next, the performance of the trained model was evaluated utilizing the quality scores obtained in the subjective listening tests undertaken remotely over the Internet. The listening tests were carried out in compliance with the MUSHRA recommendation (ITU-R BS.1534-3). In this study, the following three convolutional neural networks were compared: (1) a model employing 1D convolutional filters, (2) an Inception-based model, and (3) a VGG-based model. The last-mentioned model outperformed the model employing 1D convolutional filters in terms of predicting the scores from the listening tests, reaching a correlation value of 0.893. The performance of the Inception-based model was similar to that of the VGG-based model. Moreover, the VGG-based model outperformed the method employing a stacked gated-recurrent-unit-based deep learning framework, recently introduced by Mumtaz et al. (2022)