图 书 馆
作为二元分类的言语隔离问题Cocktail Party Problem as Binary Classification |
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| 课程网址: | http://videolectures.net/mlss09us_wang_cppbc/ |
| 主讲教师: | DeLiang Wang |
| 开课单位: | 俄亥俄州立大学 |
| 开课时间: | 2009-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Speech segregation, or the cocktail party problem, has proven to be extremely challenging. Part of the challenge stems from the lack of a carefully analyzed computational goal. While the separation of every sound source in a mixture is considered the gold standard, I argue that such an objective is neither realistic nor what the human auditory system does. Motivated by the auditory masking phenomenon, we have suggested instead the ideal time-frequency (T-F) binary mask as a main goal for computational auditory scene analysis. Ideal binary masking retains the mixture energy in T-F units where the local signal-to-noise ratio exceeds a certain threshold, and rejects the mixture energy in other T-F units. Recent psychophysical evidence shows that ideal binary masking leads to large speech intelligibility improvements in noisy environments for both normal-hearing and hearing-impaired listeners. The effectiveness of the ideal binary mask implies that sound separation may be formulated as a case of binary classification, which opens the cocktail party problem to a variety of pattern classification and clustering methods. As an example, I discuss a recent system that segregates unvoiced speech by supervised classification of acoustic-phonetic features. |
| 关 键 词: | 言语隔离; 黄金标准; 理想时间 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-23：cwx |
| 阅读次数: | 55 |