Scalable Speech Coding for IP Networks: Beyond iLBC

Koji Seto, T. Ogunfunmi
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引用次数: 10

Abstract

High quality speech at low bit rates makes code excited linear prediction (CELP) the dominant choice for a narrowband coding technique despite the susceptibility to packet loss. One of the few techniques which received attention after the introduction of CELP coding technique is the internet low bitrate codec (iLBC) because of inherent high robustness to packet loss. Addition of rate flexibility and scalability makes the iLBC an attractive choice for voice communication over IP networks. In this paper, performance improvement schemes of multi-rate iLBC and its scalable structure are proposed, and the proposed codec enhanced from the previous work is re-designed based on the subjective listening quality instead of the objective quality. In particular, perceptual weighting and the modified discrete cosine transform (MDCT) with short overlap in weighted signal domain are employed along with the improved packet loss concealment (PLC) algorithm. The subjective evaluation results show that the speech quality of the proposed codec is equivalent to that of state-of-the-art codec, G.718, under both a clean channel condition and lossy channel conditions. This result is significant considering that development of the proposed codec is still in early stage.
IP网络的可扩展语音编码:超越iLBC
低比特率下的高质量语音使得编码激发线性预测(CELP)成为窄带编码技术的主要选择,尽管它容易丢包。internet低比特率编解码器(internet low bitrate codec, iLBC)由于其对丢包具有较高的鲁棒性,在引入CELP编码技术后受到关注。速率灵活性和可扩展性使iLBC成为IP网络上语音通信的一个有吸引力的选择。本文提出了多速率iLBC及其可扩展结构的性能改进方案,并基于主观聆听质量而不是客观聆听质量对所提出的编解码器进行了重新设计。该算法采用感知加权和加权信号域短重叠的改进离散余弦变换(MDCT)以及改进的丢包隐藏(PLC)算法。主观评价结果表明,无论在干净信道条件下还是在有损信道条件下,所提编解码器的语音质量都与目前最先进的G.718编解码器相当。考虑到所提出的编解码器的开发仍处于早期阶段,这一结果意义重大。
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来源期刊
IEEE Transactions on Audio Speech and Language Processing
IEEE Transactions on Audio Speech and Language Processing 工程技术-工程:电子与电气
自引率
0.00%
发文量
0
审稿时长
24.0 months
期刊介绍: The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.
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