Impact of Packet Loss Location on Perceived Speech Quality

Slides:



Advertisements
Similar presentations
1 Case study of PESQ performance in live wireless mobile VoIP environment Zizhi Qiao (Motorola UK) Dr. Lingfen Sun (University of Plymouth, UK) Prof. Emmanuel.
Advertisements

Speech Processing for NSR Vs DSR Veeru Ramaswamy PhD CTO, Vianix LLC
STQ Workshop, Sophia-Antipolis, February 11 th, 2003 Packet loss concealment using audio morphing Franck Bouteille¹ Pascal Scalart² Balazs Kövesi² ¹ PRESCOM.
Tuning Skype Redundancy Control Algorithm for User Satisfaction Te-Yuan Huang, Kuan-Ta Chen, Polly Huang Proceedings of the IEEE Infocom Conference Rio.
A STUDY OF DESIGN COMPROMISES FOR SPEECH CODERS IN PACKET NETWORKS 1.INTRODUCTION In voice over packet networks, the coding gain achieved by prediction-based.
Voice over the Internet (the basics) CS 7270 Networked Applications & Services Lecture-2.
1 TAC2000/ IP Telephony Lab Perceptual Evaluation of Speech Quality (PESQ) Speaker: Wen-Jen Lin Date: Dec
6/3/20151 Voice Transformation : Speech Morphing Gidon Porat and Yizhar Lavner SIPL – Technion IIT December
RECONSTRUCTION OF MISSING PACKETS FOR CELP-BASED SPEECH CODERS Aamir Husain and Vladimir Cuperman School of Engineering Science, Simon Fraser University.
University of Illinois, Urbana-Champaign
Recursive End-to-end Distortion Estimation with Model-based Cross-correlation Approximation Hua Yang, Kenneth Rose Signal Compression Lab University of.
Impact of Reference Distance for Motion Compensation Prediction on Video Quality ACM/SPIE Multimedia Computing and Networking (MMCN) San Jose, California,
Introduction to Image Quality Assessment
QoS Measurement and Management for Multimedia Services Thesis Proposal Wenyu Jiang April 29, 2002.
Adaptive Delay Concealment for Internet Voice Applications with Packet-Based Time-Scale Modification Fang Liu, JongWon Kim, C.-C. Jay Kuo IEEE ICASSP 2001.
1 E-Model & MOS Speaker: Cheng-lin Tsai Adviser: Quincy Wu Date:2009/07/02.
Voice Quality Evaluation for Wireless Transmission with ROHC S. Rein and F.H.P. Fitzek and M. Reisslein Voice Quality Evaluation for Wireless Transmission.
Video Streaming: An FEC-Based Novel Approach Jianfei Cai, Chang Wen Chen Electrical and Computer Engineering, Canadian Conference on.
Nov. 3, 2000 Adaptive Playout Scheduling in Packet Voice Communications.
© 2006 Cisco Systems, Inc. All rights reserved. 2.2: Digitizing and Packetizing Voice.
H. Sanneck*, A. Stenger, K. Ben Younes, B. Girod
1 Media Gateway Benoit Bégué 2006 Study for EE department. EE526 with Professor Dan Keun Sung.
Objective and Subjective Degradations of Transcoded Voice for Heterogeneous Radio Networks Interoperability Ľubica Blašková 1, Jan Holub 1, Michael Street.
Copyright Telchemy Inc 2001 Embedded Passive Monitoring of Voice over IP Service Quality Alan Clark Telchemy, Inc Atlanta, GA
Content Classification Based on Objective Video Quality Evaluation for MPEG4 Video Streaming over Wireless Networks Asiya Khan, Lingfen Sun & Emmanuel.
Secure Steganography in Audio using Inactive Frames of VoIP Streams
Improving Voice Quality in International Mobile-to-Mobile Calls Aram Falsafi, Seattle, WA PIMRC September 2008.
Voice Over Packet Networks Getting the most from your voice codec Philippe Gournay VoiceAge Corp. 750 Lucerne Road, Suite 250 Montreal (Quebec) H3R 2H6.
Performance Evaluation of VoIP in Different Settings Tom Christiansen Ioannis Giotis Shobhit Raj Mathur.
Evalvid overview. Contents Introduction Framework and Design Functionalities Tools.
Tratamiento Digital de Voz Prof. Luis A. Hernández Gómez ftp.gaps.ssr.upm.es/pub/TDV/DOC/ Tema2c.ppt Dpto. Señales, Sistemas y Radiocomunicaciones.
Page 0 of 23 MELP Vocoders Nima Moghadam SN#: Saeed Nari SN#: Supervisor Dr. Saameti April 2005 Sharif University of Technology.
1 Requirements for the Transmission of Streaming Video in Mobile Wireless Networks Vasos Vassiliou, Pavlos Antoniou, Iraklis Giannakou, and Andreas Pitsillides.
An Empirical Evaluation of VoIP Playout Buffer Dimensioning in Skype, Google Talk, and MSN Messenger Chen-Chi Wu, Kuan-Ta Chen, Yu-Chun Chang, and Chin-Laung.
Content Clustering Based Video Quality Prediction Model for MPEG4 Video Streaming over Wireless Networks Asiya Khan, Lingfen Sun & Emmanuel Ifeachor 16.
© 2006 Cisco Systems, Inc. All rights reserved. Optimizing Converged Cisco Networks (ONT) Module 2: Cisco VoIP Implementations.
New Models for Perceived Voice Quality Prediction and their Applications in Playout Buffer Optimization for VoIP Networks University of Plymouth United.
Department of Communication and Electronic Engineering University of Plymouth, U.K. Lingfen Sun Emmanuel Ifeachor New Methods for Voice Quality Evaluation.
University of Plymouth United Kingdom {L.Sun; ICC 2002, New York, USA1 Lingfen Sun Emmanuel Ifeachor Perceived Speech Quality.
LOG Objectives  Describe some of the VoIP implementation challenges such as Delay/Latency, Jitter, Echo, and Packet Loss  Describe the voice encoding.
“Compensating for Packet Loss in Real-Time Applications“
1.INTRODUCTION The use of the adaptive codebook (ACB) in CELP-like speech coders allows the achievement of high quality speech, especially for voiced segments.
IPTEL'2001, New York, USA1 Lingfen Sun Graham Wade, Benn Lines Emmanuel Ifeachor University of Plymouth, U.K. Impact of Packet Loss Location on Perceived.
A Robust Luby Transform Encoding Pattern-Aware Symbol Packetization Algorithm for Video Streaming Over Wireless Network Dongju Lee and Hwangjun Song IEEE.
Present document contains informations proprietary to France Telecom. Accepting this document means for its recipient he or she recognizes the confidential.
Juan J. Ramos-Muñoz, Angel M. Gómez, Juan M. Lopez-Soler Intelligibility Evaluation of a VoIP Multi-flow Block Interleaver Signal Theory, Telematics and.
Voice Sampling. Sampling Rate Nyquist’s theorem states that a signal can be reconstructed if it is sampled at twice the maximum frequency of the signal.
Alan Clark Telchemy Modeling the effects of Burst Packet Loss and Recency on Subjective Voice Quality Alan Clark Telchemy
Quality of Service for Real-Time Network Management Debbie Greenstreet Product Management Director Texas Instruments.
Institut für Nachrichtengeräte und Datenverarbeitung Prof. Dr.-Ing. P. Vary On the Use of Artificial Bandwidth Extension Techniques in Wideband Speech.
1 Video and Voice over IP performance over a Satellite link Bob Dixon, Ohio State University/OARnet Prasad Calyam, OARnet Joint Techs Workshops, Columbus,
Using Speech Recognition to Predict VoIP Quality
Voice Quality in IP Telephony
Voice Performance Measurement and related technologies
VoIP over Wireless Networks
Scalable Speech Coding for IP Networks
Mohamed Chibani, Roch Lefebvre and Philippe Gournay
Wenyu Jiang , Henning Schulzrinne 이주경
Understanding the Internet Low Bit Rate Coder
Scalable Speech Coding for IP Networks: Beyond iLBC
Packet loss concealment using audio morphing
An Analytic Comparison of RPS Video Repair
Audio Henning Schulzrinne Dept. of Computer Science
Video capacity of WLANs with a multiuser perceptual quality constraint
Muhammad Niswar Graduate School of Information Science
Kyoungwoo Lee, Minyoung Kim, Nikil Dutt, and Nalini Venkatasubramanian
Quality of Service for TDR Traffic
Project proposal Multi-stream and multi-path audio transmission
Investigation of Voice Traffic in Wi-Fi Environment
Presentation transcript:

Impact of Packet Loss Location on Perceived Speech Quality Lingfen Sun Graham Wade, Benn Lines Emmanuel Ifeachor University of Plymouth, U.K. {L.F.Sun@jack.see.plym.ac.uk} {j.wade,B.Lines,E.Ifeachor@plym.ac.uk} IPTEL'2001, New York, USA

Outline Introduction Codec's internal concealment and convergence time Perceptual speech quality measurement Simulation system Loss location with perceived quality Loss location with convergence time Conclusions and future work IPTEL'2001, New York, USA

Introduction End-to-end speech transmission quality SCN IP Network Gateway End-to-end speech transmission quality IP network performance (e.g. packet loss and jitter) Gateway/terminal (codec + loss/jitter compensation) Impact of packet loss on perceived speech quality Loss pattern (e.g. burst/random) Loss location (codec's concealment) IPTEL'2001, New York, USA

Introduction (cont.) Previous research on loss location Questions: Concealment performance is speech content related (e.g. voiced/unvoiced) Analysis based on MSE or SNR for limited codec Perceptual objective methods only to assess overall quality under stochastic loss simulations Questions: How does a packet loss location affect perceived speech quality ? How does a packet loss location affect codec's convergence time (for loss constraint)? IPTEL'2001, New York, USA

Codec's internal concealment What is codec's concealment? When a loss occurs, the decoder interpolates the parameters for the lost frame from parameters of previous frames. Which codec has concealment algorithm? G.729/G.723.1/AMR (main VoIP codecs) CELP analysis-by-synthesis What are the limitations of concealment algorithms? During unvoiced(u) or voiced(v) During u/v IPTEL'2001, New York, USA

Codec's convergence time What is convergence time? The time taken by decoder to resynchronize its state with encoder after a loss occurs. It is also called resynchronization time. For set up loss constraint distance between two consecutive losses for new packet loss metrics What is the relationship between convergence time with loss location, codec type and packet size? IPTEL'2001, New York, USA

Perceptual quality measurement Reference signal System/network under test Objective perceptual quality test Objective MOS Degraded signal Transform the signal into the psychophysical representation approximating human perception Calculating their perceptual difference Mapping to objective MOS (Mean Opinion Score) Algorithms: PSQM/PSQM+/MNB/EMBSD/PESQ IPTEL'2001, New York, USA

Simulation System encoder decoder convengence time analysis loss Reference speech Degraded speech without loss Bitstream encoder decoder convengence time analysis Degraded speech with loss loss simulation decoder perceptual quality measure Reference speech Perceptual speech quality analysis with loss location Convergence time analysis with loss location IPTEL'2001, New York, USA

Speech test sentence Speech test sentence is about 6 seconds. First talkspurt (about 1.34 second, above waveform) is used for loss location analysis. Four voiced segments, V(1) to V(4), which can be decided by pitch delay in G.729 codec IPTEL'2001, New York, USA

Pitch delay from G.729 codec V(1) V(2) V(3) V(4) IPTEL'2001, New York, USA

Loss location with perceived quality Each time only one packet loss is created Loss position moves from left to right one frame by one frame Overall perceptual quality is measured from PSQM/PSQM+, MNB and EMBSD Packet size: 1 to 4 frames/packet Codec: G.729/G.723.1/AMR How does a loss location affect perceived speech quality ? IPTEL'2001, New York, USA

Loss position with quality (1) reference speech PSQM+ degraded speech PSQM IPTEL'2001, New York, USA

Loss position with quality (2) reference speech PSQM+ degraded speech PSQM IPTEL'2001, New York, USA

Loss position with quality (3) reference speech PSQM+ degraded speech PSQM IPTEL'2001, New York, USA

Loss position with quality (4) reference speech Loss position degraded speech PSQM+ PSQM IPTEL'2001, New York, USA

Overall PSQM+ vs loss location (G.729) IPTEL'2001, New York, USA

Overall MNB vs loss location (G.729) IPTEL'2001, New York, USA

Overall EMBSD vs loss location (G.729) IPTEL'2001, New York, USA

Overall PSQM+ vs loss location (G.723.1) IPTEL'2001, New York, USA

Loss location with perceived quality Loss location affects perceived quality. The loss at unvoiced speech segment has no obvious impact on perceived quality. The loss at the beginning of the voiced segment has the most severe impact on perceived quality. PSQM+ yields the most detailed result comparing to MNB/EMBSD IPTEL'2001, New York, USA

Convergence time based on MSE IPTEL'2001, New York, USA

Convergence time based on PSQM+ IPTEL'2001, New York, USA

Convergence time based on PSQM+ IPTEL'2001, New York, USA

Loss location with convergence time Convergence time is almost the same for different packet size Convergence time for a loss at unvoiced segments appears stable Convergence time shows a good linear relationship for loss at the voiced segments maximum at the beginning linear descending Up bound to the end of voiced segments IPTEL'2001, New York, USA

Conclusions and future work Investigated the impact of loss locations on perceived speech quality Investigated the impact of loss locations on convergence time The results will be helpful to develop a perceptually relevant packet loss metric. Future work will focus on more extensive analysis of the impact of packet loss on speech content IPTEL'2001, New York, USA