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Journal Article
Adaptive Speech Streaming Based on Packet Loss Prediction Using Support Vector Machine for Software-Based Multipoint Control Unit over IP Networks
- Authors
- Jin Ah Kang, Mikyong Han, Jong-Hyun Jang, Hong Kook Kim
- Issue Date
- 2016-12
- Citation
- ETRI Journal, v.38, no.6, pp.1064-1073
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- An adaptive speech streaming method to improve the perceived speech quality of a software-based multipoint control unit (SW-based MCU) over IP networks is proposed. First, the proposed method predicts whether the speech packet to be transmitted is lost. To this end, the proposed method learns the pattern of packet losses in the IP network, and then predicts the loss of the packet to be transmitted over that IP network. The proposed method classifies the speech signal into different classes of silence, unvoiced, speech onset, or voiced frame. Based on the results of packet loss prediction and speech classification, the proposed method determines the proper amount and bitrate of redundant speech data (RSD) that are sent with primary speech data (PSD) in order to assist the speech decoder to restore the speech signals of lost packets. Specifically, when a packet is predicted to be lost, the amount and bitrate of the RSD must be increased through a reduction in the bitrate of the PSD. The effectiveness of the proposed method for learning the packet loss pattern and assigning a different speech coding rate is then demonstrated using a support vector machine and adaptive multirate-narrowband, respectively. The results show that as compared with conventional methods that restore lost speech signals, the proposed method remarkably improves the perceived speech quality of an SW-based MCU under various packet loss conditions in an IP network.
- KSP Keywords
- Coding rate, Control Unit, Conventional methods, IP networks, Packet loss prediction, Speech Signals, Speech classification, Speech coding, Support VectorMachine(SVM), perceived speech quality, vector machine(LSSVM)