Estimation of Frame Independent and Enhancement Components for Speech Communication over Packet Networks

Daniele Giacobello, Manohar N. Murthi, Mads Græsbøll Christensen, Søren Holdt Jensen, Marc Moonen

Research output: Contribution to journalConference article in JournalResearchpeer-review

1 Citation (Scopus)
400 Downloads (Pure)

Abstract

In this paper, we describe a new approach to cope with packet loss in speech coders. The idea is to split the information present in each speech packet into two components, one to independently decode the given speech frame and one to enhance it by exploiting interframe dependencies. The scheme is based on sparse linear prediction and a redefinition of the analysis-by-synthesis process. We present
Mean Opinion Scores for the presented coder with different degrees of packet loss and show that it performs similarly to frame dependent coders for low packet loss probability and similarly to frame independent coders for high packet loss probability. We also present ideas on how to make the coder work synergistically with the channel loss estimate.
Original languageEnglish
JournalI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
Volume2010
Pages (from-to)4682 - 4685
Number of pages4
ISSN1520-6149
DOIs
Publication statusPublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing - Dallas, United States
Duration: 14 Mar 201017 Mar 2010

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing
CountryUnited States
CityDallas
Period14/03/201017/03/2010

Fingerprint

Speech communication
Packet networks
Packet loss

Cite this

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title = "Estimation of Frame Independent and Enhancement Components for Speech Communication over Packet Networks",
abstract = "In this paper, we describe a new approach to cope with packet loss in speech coders. The idea is to split the information present in each speech packet into two components, one to independently decode the given speech frame and one to enhance it by exploiting interframe dependencies. The scheme is based on sparse linear prediction and a redefinition of the analysis-by-synthesis process. We presentMean Opinion Scores for the presented coder with different degrees of packet loss and show that it performs similarly to frame dependent coders for low packet loss probability and similarly to frame independent coders for high packet loss probability. We also present ideas on how to make the coder work synergistically with the channel loss estimate.",
author = "Daniele Giacobello and Murthi, {Manohar N.} and Christensen, {Mads Gr{\ae}sb{\o}ll} and Jensen, {S{\o}ren Holdt} and Marc Moonen",
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doi = "10.1109/ICASSP.2010.5495189",
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Estimation of Frame Independent and Enhancement Components for Speech Communication over Packet Networks. / Giacobello, Daniele; Murthi, Manohar N.; Christensen, Mads Græsbøll; Jensen, Søren Holdt; Moonen, Marc.

In: I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, Vol. 2010, 2010, p. 4682 - 4685.

Research output: Contribution to journalConference article in JournalResearchpeer-review

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AU - Christensen, Mads Græsbøll

AU - Jensen, Søren Holdt

AU - Moonen, Marc

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AB - In this paper, we describe a new approach to cope with packet loss in speech coders. The idea is to split the information present in each speech packet into two components, one to independently decode the given speech frame and one to enhance it by exploiting interframe dependencies. The scheme is based on sparse linear prediction and a redefinition of the analysis-by-synthesis process. We presentMean Opinion Scores for the presented coder with different degrees of packet loss and show that it performs similarly to frame dependent coders for low packet loss probability and similarly to frame independent coders for high packet loss probability. We also present ideas on how to make the coder work synergistically with the channel loss estimate.

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JF - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

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