Objective Assessment of Perceptual Audio Quality Using ViSQOLAudio

Digital audio broadcasting services transmit substantial amounts of data that is encoded to minimize bandwidth whilst maximizing user quality of experience. Many large service providers continually alter codecs to improve the encoding process. Performing subjective tests to validate each codec alteration would be impractical, necessitating the use of objective perceptual audio quality models. This paper evaluates the quality scores from ViSQOLAudio, an objective perceptual audio quality model, against the quality scores of PEAQ, POLQA, and PEMO-Q on three datasets containing fullband audio encoded with a variety of codecs and bitrates. The results show that ViSQOLAudio was more accurate than all other models on two of the datasets and performed well on the third, demonstrating the utility of ViSQOLAudio for predicting the perceptual audio quality for encoded music

AMBIQUAL – a Full Reference Objective Quality Metric for Ambisonic Spatial Audio

Streaming spatial audio over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience. Streaming service providers such as YouTube need a perceptually relevant objective audio quality metric to monitor users’ perceived quality and spatial localization accuracy. In this paper we introduce a full reference objective spatial audio quality metric, AMBIQUAL, which assesses both Listening Quality and Localization Accuracy. In our solution both metrics are derived directly from the B-format Ambisonic audio. The metric extends and adapts the algorithm used in ViSQOLAudio, a full reference objective metric designed for assessing speech and audio quality. In particular, Listening Quality is derived from the omnidirectional channel and Localization Accuracy is derived from a weighted sum of similarity from B-format directional channels. This paper evaluates whether the proposed AMBIQUAL objective spatial audio quality metric can predict two factors: Listening Quality and Localization Accuracy by comparing its predictions with results from MUSHRA subjective listening tests. In particular, we evaluated the Listening Quality and Localization Accuracy of First and Third-Order Ambisonic audio compressed with the OPUS 1.2 codec at various bitrates (i.e. 32, 128 and 256, 512kbps respectively). The sample set for the tests comprised both recorded and synthetic audio clips with a wide range of time-frequency characteristics. To evaluate Localization Accuracy of compressed audio a number of fixed and dynamic (moving vertically and horizontally) source positions were selected for the test samples. Results showed a strong correlation (PCC=0.919; Spearman=0.882 regarding Listening Quality and PCC=0.854; Spearman=0.842 regarding Localization Accuracy) between objective quality scores derived from the B-format Ambisonic audio using AMBIQUAL and subjective scores obtained during listening MUSHRA tests. AMBIQUAL displays very promising quality assessment predictions for spatial audio. Future work will optimise the algorithm to generalise and validate it for any Higher Order Ambisonic formats

Towards improving ViSQOL (Virtual Speech Quality Objective Listener) Using Machine Learning Techniques

Vast amounts of sound data are transmitted every second over digital networks. VoIP services and cellular networks transmit speech data in increasingly greater volumes. Objective sound quality models provide an essential function to measure the quality of this data in real-time. However, these models can suffer from a lack of accuracy with various degradations over networks. This research uses machine learning techniques to create one support vector regression and three neural network mapping models for use with ViSQOLAudio. Each of the mapping models (including ViSQOL and ViSQOLAudio) are tested against two separate speech datasets in order to comparatively study accuracy results. Despite the slight cost in positive linear correlation and slight increase in error rate, the study finds that a neural network mapping model with ViSQOLAudio provides the highest levels of accuracy in objective speech quality measurement. In some cases, the accuracy levels can be over double that of ViSQOL. The research demonstrates that ViSQOLAudio can be altered to provide an objective speech quality metric greater than that of ViSQOL

NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder

The development of models for quality prediction of both audio and video signals is a fairly mature field. But, although several multimodal models have been proposed, the area of audio-visual quality prediction is still an emerging area. In fact, despite the reasonable performance obtained by combination and parametric metrics, currently there is no reliable pixel-based audio-visual quality metric. The approach presented in this work is based on the assumption that autoencoders, fed with descriptive audio and video features, might produce a set of features that is able to describe the complex audio and video interactions. Based on this hypothesis, we propose a No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder (NAViDAd). The model visual features are natural scene statistics (NSS) and spatial-temporal measures of the video component. Meanwhile, the audio features are obtained by computing the spectrogram representation of the audio component. The model is formed by a 2-layer framework that includes a deep autoencoder layer and a classification layer. These two layers are stacked and trained to build the deep neural network model. The model is trained and tested using a large set of stimuli, containing representative audio and video artifacts. The model performed well when tested against the UnB-AV and the LiveNetflix-II databases. %Results shows that this type of approach produces quality scores that are highly correlated to subjective quality scores.Comment: 5 page

METHODS OF QUALITY CONTROL OF PHONOGRAMS DURING RESTORATION AND RECOVERY

The object of research. Process of phonograms restoration and recovery are described. Investigated problem. Differences between some methods of quality control of phonograms during and after their restoration and recovery were investigated. The main scientific results. An instrumental method for objective assessment of the quality of phonograms is proposed, based on a non-intrusive model with parametric modeling of the phonogram signal to assess the effect of an artifact on a phonogram. The area of practical use of the research results. The results of the operational control of objective quality indicators of a real sound signal using virtual measuring instruments built into the software for working with sound are considered. An innovative technological product: a technology for assessing the quality of phonograms in the process of restoration and recovery (R&R), which makes it possible to objectively assess the quality of phonograms, taking into account artifacts of phonograms caused by the method of recording phonograms, the conditions of their storage, etc. enough high quality restored audio content. Scope of application of the innovative technological product: studio of restoration and recovery of sound phonograms on analog media, recording studios, technological processes of conversion and processing of sound programs, archives of radio and television recordings

Perceived Audio Quality Analysis in Digital Audio Broadcasting Plus System Based on PEAQ

Broadcasters need to decide on bitrates of the services in the multiplex transmitted via Digital Audio Broadcasting Plus system. The bitrate should be set as low as possible for maximal number of services, but with high quality, not lower than in conventional analog systems. In this paper, the objective method Perceptual Evaluation of Audio Quality is used to analyze the perceived audio quality for appropriate codecs --- MP2 and AAC offering three profiles. The main aim is to determine dependencies on the type of signal --- music and speech, the number of channels --- stereo and mono, and the bitrate. Results indicate that only MP2 codec and AAC Low Complexity profile reach imperceptible quality loss. The MP2 codec needs higher bitrate than AAC Low Complexity profile for the same quality. For the both versions of AAC High-Efficiency profiles, the limit bitrates are determined above which less complex profiles outperform the more complex ones and higher bitrates above these limits are not worth using. It is shown that stereo music has worse quality than stereo speech generally, whereas for mono, the dependencies vary upon the codec/profile. Furthermore, numbers of services satisfying various quality criteria are presented

Probabilistic Models of Speech Quality

東京電機大学202

Measuring and Monitoring Speech Quality for Voice over IP with POLQA, ViSQOL and P.563

There are many types of degradation which can occur in Voice over IP (VoIP) calls. Of interest in this work are degradations which occur independently of the codec, hardware or network in use. Specifically, their effect on the subjective and objec- tive quality of the speech is examined. Since no dataset suit- able for this purpose exists, a new dataset (TCD-VoIP) has been created and has been made publicly available. The dataset con- tains speech clips suffering from a range of common call qual- ity degradations, as well as a set of subjective opinion scores on the clips from 24 listeners. The performances of three ob- jective quality metrics: POLQA, ViSQOL and P.563, have been evaluated using the dataset. The results show that full reference metrics are capable of accurately predicting a variety of com- mon VoIP degradations. They also highlight the outstanding need for a wideband, single-ended, no-reference metric to mon- itor accurately speech quality for degradations common in VoIP scenarios