文件名称:speech reconstruction+SLP
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This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate
via least squares a segment of missing samples by applying the linear
prediction (LP) model of speech. First, we show that the use of a single
high-order linear predictor can provide better results than the classic
LSAR techniques based on short- and long-term predictors without the
need of a pitch detector. However, this high-order predictor may reduce
the reconstruction performance due to estimation errors, especially in the
case of short pitch periods, and non-stationarity. In order to overcome
these problems, we propose the use of a sparse linear predictor which
resembles the classical speech model, based on short- and long-term correlations, where many LP coefficients are zero. The experimental results
show the superiority of the proposed approach in both signal to noise
ratio and perceptual performance.相关搜索： compressIve
sensing
via least squares a segment of missing samples by applying the linear
prediction (LP) model of speech. First, we show that the use of a single
high-order linear predictor can provide better results than the classic
LSAR techniques based on short- and long-term predictors without the
need of a pitch detector. However, this high-order predictor may reduce
the reconstruction performance due to estimation errors, especially in the
case of short pitch periods, and non-stationarity. In order to overcome
these problems, we propose the use of a sparse linear predictor which
resembles the classical speech model, based on short- and long-term correlations, where many LP coefficients are zero. The experimental results
show the superiority of the proposed approach in both signal to noise
ratio and perceptual performance.相关搜索： compressIve
sensing
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speech reconstruction+SLP.pdf