CrowdMi: Scalable and Diagnosable Mobile Voice Quality Assessment Through Wireless Analytics

Scalable and diagnosable are the two most crucial needs for voice call quality assessment in mobile networks. However, while these two requirements are widely accepted by mobile carriers, they do not receive enough attention during the development. Current related research mainly focuses on audio feature analysis, which is costly, sensitive to language and tones, and infeasible to be applied to large-scale mobile networks. In this paper, we revisit this problem, and for the first time explore wireless network, the causal factor that directly impacts the mobile voice quality but yet lacks attention for decades. We design CrowdMi, a wireless analytical tool that model the mobile voice quality by crowdsourcing and mining the network indicators of cellphones. CrowdMi mines hundreds of network indicators to build a causal relationship between voice quality and network conditions, and carefully calibrates the model according to the widely accepted perceptual objective listening quality assessment (POLQA) voice assessment standard. We implement a light-load CrowdMi Client App in Android smartphones, which automatically collects data through user crowdsourcing and outputs to the CrowdMi Server in our data center that runs the mining algorithm. We conduct a pilot trial in VoLTE network in different geographical areas and network coverages. The trial shows that the CrowdMi does not require any additional hardware or human effort, and has very high model accuracy and strong diagnosability.

[1]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[2]  Weisi Lin,et al.  Non-intrusive quality assessment for enhanced speech signals based on spectro-temporal features , 2014, 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[3]  Weiwei Zhang,et al.  A New Method of Objective Speech Quality Assessment in Communication System , 2013, J. Multim..

[4]  Emmanuel Vincent,et al.  Subjective and Objective Quality Assessment of Audio Source Separation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  Gordon L. Stuber,et al.  Principles of Mobile Communication , 1996 .

[6]  Tiago H. Falk,et al.  Hybrid Signal-and-Link-Parametric Speech Quality Measurement for VoIP Communications , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Régine Le Bouquin-Jeannès,et al.  On the Evaluation of the Conversational Speech Quality in Telecommunications , 2008, EURASIP J. Adv. Signal Process..

[8]  Martin Sauter.  From GSM to LTE: An Introduction to Mobile Networks and Mobile Broadband , 2011 .

[9]  Tiago H. Falk,et al.  A Non-Intrusive Quality and Intelligibility Measure of Reverberant and Dereverberated Speech , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Peter Reichl,et al.  The Logarithmic Nature of QoE and the Role of the Weber-Fechner Law in QoE Assessment , 2010, 2010 IEEE International Conference on Communications.

[11]  S. R. Broom,et al.  VoIP Quality Assessment: Taking Account of the Edge-Device , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Sebastian Möller,et al.  Estimation of ‘quality per call’ in modelled telephone conversations , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Patrick Bauer,et al.  On speech quality assessment of artificial bandwidth extension , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Sebastian Möller,et al.  An intrusive super-wideband speech quality model: DIAL , 2010, INTERSPEECH.

[15]  Sebastian Möller,et al.  Modeling Call Quality for Time-Varying Transmission Characteristics Using Simulated Conversational Structures , 2009 .

[16]  D. U. Ebem.  Validating perceptual objective listening quality assessment methods on the tonal language Igbo , 2009 .