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HMM-Based Multipitch Tracking for Noisy and Reverberant Speech

机译:基于HMM的多音高音调跟踪

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Multipitch tracking in real environments is critical for speech signal processing. Determining pitch in reverberant and noisy speech is a particularly challenging task. In this paper, we propose a robust algorithm for multipitch tracking in the presence of both background noise and room reverberation. An auditory front-end and a new channel selection method are utilized to extract periodicity features. We derive pitch scores for each pitch state, which estimate the likelihoods of the observed periodicity features given pitch candidates. A hidden Markov model integrates these pitch scores and searches for the best pitch state sequence. Our algorithm can reliably detect single and double pitch contours in noisy and reverberant conditions. Quantitative evaluations show that our approach outperforms existing ones, particularly in reverberant conditions.
机译:实际环境中的多音高跟踪对于语音信号处理至关重要。确定混响和嘈杂语音的音调是一项特别具有挑战性的任务。在本文中,我们提出了一种在背景噪声和房间混响同时存在的情况下用于多音高跟踪的鲁棒算法。听觉前端和新的频道选择方法用于提取周期性特征。我们推导出每个音高状态的音高分数,这些音高分数估计给定音高候选者观察到的周期性特征的可能性。一个隐式马尔可夫模型将这些音高分数整合在一起,并搜索最佳音高状态序列。我们的算法可以在嘈杂和混响条件下可靠地检测出单音高和双音高的轮廓。定量评估表明,我们的方法优于现有方法,特别是在混响条件下。

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