ICA-based efficient blind dereverberation and echo cancellation method for barge-in-able robot audition
ICA-based efficient blind dereverberation and echo cancellation method for barge-in-able robot audition
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DOI：
10.1109/icassp.2009.4960424
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发表时间：
2009-04
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通讯作者：
Ryu Takeda;K. Nakadai;Toru Takahashi;Kazunori Komatani;T. Ogata;HIroshi G. Okuno
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作者：
Ryu Takeda;K. Nakadai;Toru Takahashi;Kazunori Komatani;T. Ogata;HIroshi G. Okuno
本文描述了一种允许在不同环境下进行机器人试听的新方法。“强插”指的是用户在机器人说话的同时开始说话。为了实现这一功能，必须同时处理盲去混响和回声消除问题。我们采用独立成分分析(ICA)是因为它本质上为这两个问题提供了一个自然的框架。为了处理混响，我们将基于多输入/输出逆滤波定理的观测模型应用于频域ICA。其主要问题是ICA的计算代价较高。我们通过两种技术将计算复杂度降低到混响时间的线性数量级：1)基于观测信号独立性的分离模型；2)加强空间扩展进行预处理。实验结果表明，我们的方法将混响语音的单词正确率提高了10-20个百分点。
This paper describes a new method that allows “Barge-In” in various environments for robot audition. “Barge-in” means that a user begins to speak simultaneously while a robot is speaking. To achieve the function, we must deal with problems on blind dereverberation and echo cancellation at the same time. We adopt Independent Component Analysis (ICA) because it essentially provides a natural framework for these two problems. To deal with reverberation, we apply a Multiple Input/Output INverse-filtering Theorem-based model of observation to the frequency domain ICA. The main problem is its high-computational cost of ICA. We reduce the computational complexity to the linear order of reverberation time by using two techniques: 1) a separation model based on observed signal independence, and 2) enforced spatial sphering for preprocessing. The experimental results revealed that our method improved word correctness of reverberant speech by 10–20 points.