Quality of experience evaluation of voice communication: an affect-based approach

Abstract

Voice communication systems such as Voice-over IP (VoIP), Public Switched Telephone Networks, and Mobile Telephone Networks, are an integral means of human tele-interaction. These systems pose distinctive challenges due to their unique characteristics such as low volume, burstiness and stringent delay/loss requirements across heterogeneous underlying network technologies. Effective quality evaluation methodologies are important for system development and refinement, particularly by adopting user feedback based measurement. Presently, most of the evaluation models are system-centric (Quality of Service or QoS-based), which questioned us to explore a user-centric (Quality of Experience or QoE-based) approach as a step towards the human-centric paradigm of system design. We research an affect-based QoE evaluation framework which attempts to capture users' perception while they are engaged in voice communication. Our modular approach consists of feature extraction from multiple information sources including various affective cues and different classification procedures such as Support Vector Machines (SVM) and k-Nearest Neighbor (kNN). The experimental study is illustrated in depth with detailed analysis of results. The evidences collected provide the potential feasibility of our approach for QoE evaluation and suggest the consideration of human affective attributes in modeling user experience.

A low computational complexity V-BLAST/STBC detection mechanism in MIMO system

“…The V-BLAST layer data symbols are decoded with the properties of upper triangular square matrix R in (13) and first estimated symbols of STBC layer. These layer symbols in the first and second time slot are then derived in a successive way aŝ…”

“…The received V-BLAST/STBC matrix is given byỹ ¼ Rx þz as in (13). Hence, the received sub-data streams of V-BLAST layer in the first and second time slot are given bỹ…”

“…Although Alamouti's STBC provides full transmit and receive antenna diversity, the maximum code rate of one can be achieved for two transmit antennas only. For more than two antennas, the maximum possible code rate is 3/4 [11], thus Alamouti's STBC could not satisfy the demand of the desired high system capacity in real time system with good quality of service (QoS) [12,13]. Therefore, in order to achieve spatial multiplexing and diversity gains simultaneously, the hybrid MIMO system has been introduced [14][15][16].…”

Featuring the e-service quality of online website from a varied perspective

“…Bhattachary et al [6] described that effective evaluation of system performance becomes critical and is served as an important instrument for quality monitoring and management. Based on this argument, this study considers that when the shopping website has better performances that the website can satisfy the demands and expectations of customers, customers obtain better service quality.…”

On user-centric tools for QoE-based recommendation and real-time analysis of large-scale markets

“…Classification and regression methods based on machine learning, data mining, and statistical modeling algorithms have also been employed for the prediction of QoE [12,13]. U-map applies a number of state-of-the-art machine learning algorithms, develops and trains models based on the collected network measurements and user feedback, and dynamically selects the best one for predicting the QoE in a user-centric manner.…”