We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependen...
详细信息
ISBN:
(纸本)9781424442966
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependent factorial-max VQ model. One approach extends beam search, whereas the second relies on the iterated conditional modes algorithm. We compare the methods to the hierarchically structured VQ model [1] and to the full search using the Grid Corpus [2]. The first two algorithms reduce the computational costs by almost two orders of magnitude compared to full search, whereas the separation performance shows a slight and insignificant decrease in terms of target-to-masker ratio. Additionally, the heuristics are compared in terms of execution time.
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependen...
详细信息
ISBN:
(纸本)9781424442959;9781424442966
We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependent factorial-max VQ model. One approach extends beam search, whereas the second relies on the iterated conditional modes algorithm. We compare the methods to the hierarchically structured VQ model [1] and to the full search using the Grid Corpus [2]. The first two algorithms reduce the computational costs by almost two orders of magnitude compared to full search, whereas the separation performance shows a slight and insignificant decrease in terms of target-to-masker ratio. Additionally, the heuristics are compared in terms of execution time.
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