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New Distance Measure for Monaural Model-based Sound Separation

机译:基于Monaural模型的声音分离的新距离测量

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We present a new distance measure based on certain perceptual cues to improve the state-of-the-art model-based sound separation performance which has been introduced as a challenging topic for decades. Conducting several simulation results, it is demonstrated that using such an appropriate distance measure proposed in this work in place of the commonly used Euclidean distance in Vector Quantization (VQ) procedure can significantly result in a better speech modeling which is also close to transparent reconstruction in terms of naturalness. It is also observed that choosing an overlap around 25terms of both Spectral Distortion Ratio (SDR) and Segmental Signal to Noise Ratio (SSNR).
机译:我们提出了一种基于某些感知提示的新距离措施,以改善基于最先进的基于模型的声音分离性能,这已被引入到几十年充满挑战性主题。进行多个模拟结果,证明使用在该工作中提出的这种适当的距离测量来代替矢量量化中的常用的欧几里德距离(VQ)过程可以显着导致更好的语音建模,这也靠近透明的重建自然术语。还观察到,在光谱失真比(SDR)和分段信号的25TER围绕25terms的重叠选择与噪声比(SSNR)。

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