Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Other Computer Engineering (33)
- Electrical and Computer Engineering (28)
- Digital Communications and Networking (26)
- Computer and Systems Architecture (17)
- Arts and Humanities (12)
-
- Music (9)
- Electrical and Electronics (8)
- Other Music (8)
- Signal Processing (7)
- Systems and Communications (7)
- Architectural Engineering (6)
- Architectural Technology (6)
- Architecture (6)
- Civil and Environmental Engineering (6)
- Computer Sciences (6)
- Construction Engineering (6)
- Construction Engineering and Management (6)
- Physical Sciences and Mathematics (6)
- Social and Behavioral Sciences (6)
- Civil Engineering (5)
- Other Civil and Environmental Engineering (5)
- Other Engineering (5)
- Education (4)
- Environmental Engineering (4)
- Robotics (4)
- Communication (3)
- Data Storage Systems (3)
- Databases and Information Systems (3)
- Keyword
-
- Data sonification (8)
- SDN (7)
- Machine Learning (6)
- Contour icons (5)
- Deep Learning (5)
-
- POLQA (5)
- Machine learning (4)
- NSIM (4)
- ViSQOL (4)
- VoIP (4)
- Auditory periphery model (3)
- Building Information Modelling (3)
- Deep learning (3)
- Dna sonification (3)
- Education (3)
- Ireland (3)
- Mobile devices (3)
- Objective Speech Quality (3)
- P.853 (3)
- Software quality (3)
- Speech (3)
- Technology (3)
- Active Learning (2)
- Artificial intelligence (2)
- Authentication (2)
- Avatar (2)
- BIM (2)
- Backdoors (2)
- Chaos (2)
- Charging Stations (2)
Articles 91 - 120 of 178
Full-Text Articles in Computer Engineering
Assessing The Usefulness Of Different Feature Sets For Predicting The Comprehension Difficulty Of Text, Brian Mac Namee, John D. Kelleher, Noel Fitzpatrick
Assessing The Usefulness Of Different Feature Sets For Predicting The Comprehension Difficulty Of Text, Brian Mac Namee, John D. Kelleher, Noel Fitzpatrick
Conference papers
Within English second language acquisition there is an enthusiasm for using authentic text as learning materials in classroom and online settings. This enthusiasm, however, is tempered by the difficulty in finding authentic texts at suitable levels of comprehension difficulty for specific groups of learners. An automated way to rate the comprehension difficulty of a text would make finding suitable texts a much more manageable task. While readability metrics have been in use for over 50 years now they only capture a small amount of what constitutes comprehension difficulty. In this paper we examine other features of texts that are related …
Analysing The Behaviour Of Online Investors In Times Of Geopolitical Distress: A Case Study On War Stocks, James Usher, Pierpaolo Dondio
Analysing The Behaviour Of Online Investors In Times Of Geopolitical Distress: A Case Study On War Stocks, James Usher, Pierpaolo Dondio
Conference papers
In this paper we analyse how the behavior of an online financial community in time of geopolitical crises. In particular, we studied the behaviour, composition and communication patterns of online investors before and after a military geopolitical event. We selected a set of 23 key-events belonging to the 2003 US-led invasion of Iraq, the Arab Spring and the first period of the Ukraine crisis. We restricted our study to a set of eight so called military stocks, which are US-manufacturing companies active in the defence sector. We studied the resilience of the community to information shocks by comparing the community …
Clustering Opportunistic Ant-Based Routing Protocol For Wireless Sensor Networks, Xinlu Li, Brian Keegan, Fredrick Mtenzi
Clustering Opportunistic Ant-Based Routing Protocol For Wireless Sensor Networks, Xinlu Li, Brian Keegan, Fredrick Mtenzi
Conference papers
The wireless Sensor Networks (WSNs) have a wide range of applications in many ereas, including many kinds of uses such as environmental monitoring and chemical detection. Due to the restriction of energy supply, the improvement of routing performance is the major motivation in WSNs. We present a Clustering Opportunistic Ant-based Routing protocol (COAR), which comprises the following main contributions to achieve high energy efficient and well load-balance: (i) in the clustering algorithm, we caculate the theoretical value of energy dissipation, which will make the number of clusters fluctuate around the expected value, (ii) define novel heuristic function and pheromone update …
Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh
Conference papers
Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set …
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Conference papers
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency of …
Empirical Comparative Analysis Of 1-Of-K Coding And K-Prototypes In Categorical Clustering, Fei Wang, Hector Franco, John Pugh, Robert J. Ross
Empirical Comparative Analysis Of 1-Of-K Coding And K-Prototypes In Categorical Clustering, Fei Wang, Hector Franco, John Pugh, Robert J. Ross
Conference papers
Clustering is a fundamental machine learning application, which partitions data into homogeneous groups. K-means and its variants are the most widely used class of clustering algorithms today. However, the original k-means algorithm can only be applied to numeric data. For categorical data, the data has to be converted into numeric data through 1-of-K coding which itself causes many problems. K-prototypes, another clustering algorithm that originates from the k-means algorithm, can handle categorical data by adopting a different notion of distance. In this paper, we systematically compare these two methods through an experimental analysis. Our analysis shows that K-prototypes is more …
Model-Free And Model-Based Active Learning For Regression, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Model-Free And Model-Based Active Learning For Regression, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Conference papers
Training machine learning models often requires large labelled datasets, which can be both expensive and time-consuming to obtain. Active learning aims to selectively choose which data is labelled in order to minimize the total number of labels required to train an effective model. This paper compares model-free and model-based approaches to active learning for regression, finding that model-free approaches, in addition to being less computationally intensive to implement, are more effective in improving the performance of linear regressions than model-based alternatives.
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Conference papers
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency …
Bitrate Classification Of Twice-Encoded Audio Using Objective Quality Features, Colm Sloan, Damien Kelly, Naomi Harte, Anil C. Kokaram, Andrew Hines
Bitrate Classification Of Twice-Encoded Audio Using Objective Quality Features, Colm Sloan, Damien Kelly, Naomi Harte, Anil C. Kokaram, Andrew Hines
Conference papers
When a user uploads audio files to a music stream- ing service, these files are subsequently re-encoded to lower bitrates to target different devices, e.g. low bitrate for mobile. To save time and bandwidth uploading files, some users encode their original files using a lossy codec. The metadata for these files cannot always be trusted as users might have encoded their files more than once. Determining the lowest bitrate of the files allows the streaming service to skip the process of encoding the files to bitrates higher than that of the uploaded files, saving on processing and storage space. This …
Modeling Mental Workload Via Rule-Based Expert System: A Comparison With Nasa-Tlx & Workload Profile, Lucas Rizzo, Sarah Jane Delany, Pierpaolo Dondio, Luca Longo
Modeling Mental Workload Via Rule-Based Expert System: A Comparison With Nasa-Tlx & Workload Profile, Lucas Rizzo, Sarah Jane Delany, Pierpaolo Dondio, Luca Longo
Conference papers
In the last few decades several fields have made use of the construct of human mental workload (MWL) for system and task design as well as for assessing human performance. Despite this interest, MWL remains a nebulous concept with multiple definitions and measurement techniques. State-of-the-art models of MWL are usually ad-hoc, considering different pools of pieces of evidence aggregated with different inference strategies. In this paper the aim is to deploy a rule-based expert system as a more structured approach to model and infer MWL. This expert system is built upon a knowledge-base of an expert and transates into computable …
Kicm: A Knowledge-Intensive Context Model, Fredrick Mtenzi, Denis Lupiana
Kicm: A Knowledge-Intensive Context Model, Fredrick Mtenzi, Denis Lupiana
Conference papers
A context model plays a significant role in developing context-aware architectures and consequently on realizing context-awareness, which is important in today's dynamic computing environments. These architectures monitor and analyse their environments to enable context-aware applications to effortlessly and appropriately respond to users' computing needs. These applications make the use of computing devices intuitive and less intrusive. A context model is an abstract and simplified representation of the real world, where the users and their computing devices interact. It is through a context model that knowledge about the real world can be represented in and reasoned by a context-aware architecture. This …
Ant Colony Clustering Routing Protocol For Optimization Of Large Scale Wireless Sensor Networks, Li Xinlu, Brian Keegan, Fredrick Japhet
Ant Colony Clustering Routing Protocol For Optimization Of Large Scale Wireless Sensor Networks, Li Xinlu, Brian Keegan, Fredrick Japhet
Conference papers
No abstract provided.
Tcd-Voip, A Research Database Of Degraded Speech For Assessing Quality In Voip Applications, Andrew Hines, Naomi Harte, Eoin Gillen
Tcd-Voip, A Research Database Of Degraded Speech For Assessing Quality In Voip Applications, Andrew Hines, Naomi Harte, Eoin Gillen
Conference papers
There are many types of degradation which can occur in Voice over IP calls. Degradations which occur independently of the codec, hardware, or network in use are the focus of this paper. The development of new quality metrics for modern communication systems depends heavily on the availability of suitable test and development data with subjective quality scores. A new dataset of VoIP degradations (TCD-VoIP) has been created and is presented in this paper. The dataset contains speech samples with a range of common VoIP degradations, and the corresponding set of subjective opinion scores from 24 listeners The dataset is publicly …
Measuring And Monitoring Speech Quality For Voice Over Ip With Polqa, Visqol And P.563, Andrew Hines, Eoin Gillen, Naomi Harte
Measuring And Monitoring Speech Quality For Voice Over Ip With Polqa, Visqol And P.563, Andrew Hines, Eoin Gillen, Naomi Harte
Conference papers
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 …
The Impact Of Fuzzy Requirements On Medical Device Software Development, Martin Mchugh, Abder-Rahman Ali, Fergal Mccaffery
The Impact Of Fuzzy Requirements On Medical Device Software Development, Martin Mchugh, Abder-Rahman Ali, Fergal Mccaffery
Conference papers
Any software development project can experience difficulties with unclear or vague requirements. Unfortunately, this problem can be experience two fold in regulated environments such as the medical device software development industry. In the medical device software development industry, development organisations must contend with vague or “fuzzy” both the customer and regulatory bodies. As new requirements are introduced they can have a knock on effect on other requirements. These requirements should be analysed to determine if they are conflicting, cooperative, mutually exclusive and irrelevant. Only when the requirement is classified can a clear method be established as how to integrate that …
Building A Database Of Political Speech Does Culture Matter In Charisma Annotations?, Andrew Hines, Ailbhe Cullen, Naomi Harte
Building A Database Of Political Speech Does Culture Matter In Charisma Annotations?, Andrew Hines, Ailbhe Cullen, Naomi Harte
Conference papers
For both individual politicians and political parties the in- ternet has become a vital tool for self-promotion and the distribution of ideas. The rise of streaming has enabled po- litical debates and speeches to reach global audiences. In this paper, we explore the nature of charisma in political speech, with a view to automatic detection. To this end, we have collected a new database of political speech from YouTube and other on-line resources. Annotation is per- formed both by native listeners, and Amazon Mechanical Turk (AMT) workers. Detailed analysis shows that both la- bel sets are equally reliable. The results …
Clarification Dialogues For Perception-Based Errors In Situated Human-Computer Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee
Clarification Dialogues For Perception-Based Errors In Situated Human-Computer Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee
Conference papers
We present an experiment about situated human-computer interaction. Participants interacted with a simulated robot system to complete a series of tasks in a situated environment. Errors were introduced into the robot's perception to produce misunderstandings. We recorded the interactions and attempt to identify strategies the participants used to solve the arising problems.
Robustness And Prediction Accuracy Of Machine Learning For Objective Visual Quality Assessment, Andrew Hines, Paul Kendrick, Adriaan Barri, Manish Narwaria, Judith A. Redi
Robustness And Prediction Accuracy Of Machine Learning For Objective Visual Quality Assessment, Andrew Hines, Paul Kendrick, Adriaan Barri, Manish Narwaria, Judith A. Redi
Conference papers
Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reliability of ML-based techniques within objective quality assessment metrics is often questioned. In this study, the robustness of ML in supporting objective quality assessment is investigated, specifically when the feature set adopted for prediction is suboptimal. A Principal Component Regression based algorithm and a Feed Forward Neural Network are compared when pooling the Structural Similarity Index (SSIM) features perturbed with noise. The neural network adapts better with …
Perceived Audio Quality For Streaming Stereo Music, Andrew Hines, Eoin Gillen, Naomi Harte, Damien Kelly, Jan Skoglund, Anil Kokaram
Perceived Audio Quality For Streaming Stereo Music, Andrew Hines, Eoin Gillen, Naomi Harte, Damien Kelly, Jan Skoglund, Anil Kokaram
Conference papers
Users of audio-visual streaming services expect an ever increasing quality of experience. Channel bandwidth remains a bottleneck commonly addressed with lossy compression schemes for both the video and audio streams. Anecdotal evidence suggests a strongly perceived link between bit rate and quality. This paper presents three audio quality listening experiments using the ITU MUSHRA methodology to assess a number of audio codecs typically used by streaming services. They were assessed for a range of bit rates using three presentation modes: consumer and studio qual- ity headphones and loudspeakers. Our results indicate that with consumer quality headphones, listeners were not differentiating …
Judging Emotion From Low-Pass Filtered Naturalistic Emotional Speech, John Snel, Charlie Cullen
Judging Emotion From Low-Pass Filtered Naturalistic Emotional Speech, John Snel, Charlie Cullen
Conference papers
In speech, low frequency regions play a significant role in paralinguistic communication such as the conveyance of emotion or mood. The extent to which lower frequencies signify or contribute to affective speech is still an area for investigation. To investigate paralinguistic cues, and remove interference from linguistic cues, researchers can low-pass filter the speech signal on the assumption that certain acoustic cues characterizing affect are still discernible. Low-pass filtering is a practical technique to investigate paralinguistic phenomena, and is used here to investigate the inference of naturalistic emotional speech. This paper investigates how listeners perceive the level of Activation, and …
Exploring Spatial Business Data: A Roa Based Ecampus Application, Thanh Thoa Pham Thi, Linh Truong- Hong, Junjun Yin, James Carswell
Exploring Spatial Business Data: A Roa Based Ecampus Application, Thanh Thoa Pham Thi, Linh Truong- Hong, Junjun Yin, James Carswell
Conference papers
In "Smart" environments development, providing users with search utilities for interacting efficiently with web and wireless devices is a key goal. At smaller scales, Google Maps and Google Earth with satellite and street views have helped users for querying general information at specific locations. However, at larger local scales, where detailed 3D geometries linked to business data are needed, there is a recognized lack of related information and functionality for in depth exploration of an area. Linking spatial data and business data helps to enrich the user experience by fulfilling more task specific user needs. This paper presents an eCampus …
Expecting The Unexpected : Measuring Uncertainties In Mobile Robot Path Planning In Dynamic Envionments, Yan Li, Brian Mac Namee, John D. Kelleher
Expecting The Unexpected : Measuring Uncertainties In Mobile Robot Path Planning In Dynamic Envionments, Yan Li, Brian Mac Namee, John D. Kelleher
Conference papers
Unexpected obstacles pose significant challenges to mobile robot navigation. In this paper we investigate how, based on the assumption that unexpected obstacles really follow patterns that can be exploited, a mobile robot can learn the locations within an environment that are likely to contain obstacles, and so plan optimal paths by avoiding these locations in subsequent navigation tasks. We propose the DUNC (Dynamically Updating Navigational Confidence) method to do this. We evaluate the performance of the DUNC method by comparing it with existing methods in a large number of randomly generated simulated test environments. our evaluations show that, by learning …
Robustness Of Speech Quality Metrics To Background Noise And Network Degradations: Comparing Visqol, Pesq And Polqa, Andrew Hines, Naomi Harte, Jan Skoglund, Anil Kokaram
Robustness Of Speech Quality Metrics To Background Noise And Network Degradations: Comparing Visqol, Pesq And Polqa, Andrew Hines, Naomi Harte, Jan Skoglund, Anil Kokaram
Conference papers
The Virtual Speech Quality Objective Listener (ViSQOL) is a new objective speech quality model. It is a signal based full reference metric that uses a spectro-temporal measure of similarity between a reference and a test speech signal. ViSQOL aims to predict the overall quality of experience for the end listener whether the cause of speech quality degradation is due to ambient noise, or transmission channel degradations. This paper describes the algorithm and tests the model using two speech corpora: NOIZEUS and E4. The NOIZEUS corpus contains speech under a variety of background noise types, speech enhancement methods, and SNR levels. …
Detailed Comparative Analysis Of Pesq And Visqol Behaviour In The Context Of Playout Delay Adjustments Introduced By Voip Jitter Buffer Algorithms, Andrew Hines, Peter Pocta, Hugh Melvin
Detailed Comparative Analysis Of Pesq And Visqol Behaviour In The Context Of Playout Delay Adjustments Introduced By Voip Jitter Buffer Algorithms, Andrew Hines, Peter Pocta, Hugh Melvin
Conference papers
The default best-effort Internet presents significant challenges for delay-sensitive applications such as VoIP. To cope with non determinism, receiver playout strategies are utilised in VoIP applications that adapt to network condition. Such strategies can be divided into two different groups, namely per-talkspurt and per-packet. The former make use of silence periods within natural speech and adapt such silences to track network conditions, thus preserving the integrity of active speech talkspurts. Examples of this approach are described in [1, 2]. Per packet strategies are different in that adjustments are made both during silence periods and during talkspurts by time-scaling of packets, …
Monitoring The Effects Of Temporal Clipping On Voip Speech Quality, Andrew Hines, Naomi Harte, Jan Skoglund, Anil Kokaram
Monitoring The Effects Of Temporal Clipping On Voip Speech Quality, Andrew Hines, Naomi Harte, Jan Skoglund, Anil Kokaram
Conference papers
This paper presents work on a real-time temporal clipping monitoring tool for VoIP. Temporal clipping can occur as a result of voice activity detection (VAD) or echo cancellation where comfort noise in used in place of clipped speech segments. The algorithm presented will form part of a no-reference objective model for quantifying perceived speech quality in VoIP. The overall approach uses a modular design that will help pinpoint the reason for degradations in addition to quantifying their impact on speech quality. The new algorithm was tested for VAD compared over a range of thresholds and varied speech frame sizes. The …
A Crowdsourcing Approach To Labelling A Mood Induced Speech Corpus, John Snel, Alexey Tarasov, Charlie Cullen, Sarah Jane Delany
A Crowdsourcing Approach To Labelling A Mood Induced Speech Corpus, John Snel, Alexey Tarasov, Charlie Cullen, Sarah Jane Delany
Conference papers
This paper demonstrates the use of crowdsourcing to accumulate ratings from na ̈ıve listeners as a means to provide labels for a naturalistic emotional speech dataset. In order to do so, listening tasks are performed with a rating tool, which is delivered via the web. The rating requirements are based on the classical dimensions, activation and evaluation, presented to the participant as two discretised 5-point scales. Great emphasis is placed on the participant’s overall understanding of the task, and on the ease-of-use of the tool so that labelling accuracy is reinforced. The accumulation process is ongoing with a goal to …
Distributed Formal Concept Analysis Algorithms Based On An Iterative Mapreduce Framework, Ruairí De Fréin, Biao Xu, Eric Robson, Mícheál Ó Fóghlú
Distributed Formal Concept Analysis Algorithms Based On An Iterative Mapreduce Framework, Ruairí De Fréin, Biao Xu, Eric Robson, Mícheál Ó Fóghlú
Conference papers
While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganter’s classic algorithm …
Improved Speech Intelligibility With A Chimaera Hearing Aid Algorithm, Andrew Hines, Naomi Harte
Improved Speech Intelligibility With A Chimaera Hearing Aid Algorithm, Andrew Hines, Naomi Harte
Conference papers
It is recognised that current hearing aid fitting algorithms can corrupt fine timing cues in speech. This paper presents a fitting algorithm that aims to improve speech intelligibility, while preserving the temporal fine structure. The algorithm combines the signal envelope amplification from a standard hearing aid fitting algorithm with the fine timing information available to unaided listeners. The proposed “chimaera aid” is evaluated with computer simulated listener tests to measure its speech intelligibility for 3 sample hearing losses. In addition, the experiment demonstrates the potential application of auditory nerve models in the development of new hearing aid algorithm designs using …
Stereoscopic Avatar Interfaces : A Study To Determine What Effect, If Any, 3d Technology Has At Increasing The Interpretability Of An Avatar's Gaze Into The Real-World, Mark Dunne, Brian Mac Namee, John D. Kelleher
Stereoscopic Avatar Interfaces : A Study To Determine What Effect, If Any, 3d Technology Has At Increasing The Interpretability Of An Avatar's Gaze Into The Real-World, Mark Dunne, Brian Mac Namee, John D. Kelleher
Conference papers
An approach to displaying avatar interfaces monoscopically such as the Turning, Stretching and Boxing (TSB) technique (a combination of three graphical processes) has been shown to improve the communication efficiency of avatars by increasing a user's ability to interpret an avatar's gaze direction through the delivery of a sustained 3D illusion of the avatar on a standard 2D display. A reasonable question to ask about this approach is whether or not the improvement in interpretability can be matched or surpassed by using a standard 3D display technology (stereoscopic) with or without the fore-mentioned approach? This paper presents an experiment that …
Visqol: The Virtual Speech Quality Objective Listener, Andrew Hines, Jan Skoglund, Anil Kokaram, Naomi Harte
Visqol: The Virtual Speech Quality Objective Listener, Andrew Hines, Jan Skoglund, Anil Kokaram, Naomi Harte
Conference papers
A model of human speech quality perception has been developed to provide an objective measure for predicting subjective quality assessments. The Virtual Speech Quality Objective Listener (ViSQOL) model is a signal based full reference metric that uses a spectro-temporal measure of similarity between a reference and a test speech signal. This paper describes the algorithm and compares the results with PESQ for common problems in VoIP: clock drift, associated time warping and jitter. The results indicate that ViSQOL is less prone to underestimation of speech quality in both scenarios than the ITU standard.