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CONDITIONAL SPLIT LATTICE VECTOR QUANTIZATION FOR SPECTRAL ENCODING OF AUDIO SIGNALS

机译:用于音频信号频谱编码的条件分割晶格矢量量化

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In this paper we propose a novel quantization method with application to audio coding. Because the lattice truncation based quantizers are finite, not all input points have nearest neighbors within the defined truncations. The proposed conditional split lattice vector quantizer (CSLVQ) allows the possibility of splitting to lower dimensions an input point falling outside the truncation enabling thus the preservation of a low distortion, with only a local payoff in bitrate. Furthermore, the proposed quantization tool is versatile with respect to the dimension of the input data, the same quantization functions being used for different dimensions. The new quantizer has been tested for spectral encoding of real audio samples by encoding each frequency subband of the audio signal using a vector quantizer consisting of a lattice truncated following a generalized Gaussian contour of equiprobability. The results of objective listening tests show similar results to the AAC for high bitrates and clearly better results than the AAC for lower bitrates.
机译:在本文中,我们提出了一种新的量化方法,并将其应用于音频编码。由于基于格截断的量化器是有限的,因此并非所有输入点在定义的截断内都具有最近的邻居。所提出的条件分裂晶格矢量量化器(CSLVQ)允许将落在截断之外的输入点分解为较小尺寸的可能性,从而可以保持低失真,而比特率只有局部回报。此外,所提出的量化工具相对于输入数据的维度是通用的,相同的量化函数被用于不同的维度。通过使用矢量量化器对音频信号的每个频率子带进行编码,已对新量化器进行了真实音频样本的频谱编码测试,该矢量量化器由遵循等概率的广义高斯轮廓被截断的晶格组成。客观聆听测试的结果显示,对于高比特率,其结果与AAC相似,并且显然比对于低比特率的AAC的结果更好。

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