Online Non-Negative Convolutive Pattern Learning for Speech Signals
نویسندگان
چکیده
منابع مشابه
Online Pattern Learning for Non-Negative Convolutive Sparse Coding
The unsupervised learning of spectro-temporal speech patterns is relevant in a broad range of tasks. Convolutive non-negative matrix factorization (CNMF) and its sparse version, convolutive non-negative sparse coding (CNSC), are powerful, related tools. A particular difficulty of CNMF/CNSC, however, is the high demand on computing power and memory, which can prohibit their application to large ...
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Overlapping speech is known to degrade speaker diarization performance with impacts on both speech activity detection, speaker clustering and segmentation (speaker error). While previous related work has made important advances the problem remains largely unsolved. This paper reports early work to investigate the application of non-negative matrix factorisation (NMF) to the overlap problem. NMF...
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The effective handling of overlapping speech is at the limits of the current state of the art in speaker diarization. This paper presents our latest work in overlap detection. We report the combination of features derived through convolutive nonnegative sparse coding and new energy, spectral and voicingrelated features within a conventional HMM system. Overlap detection results are fully integr...
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Convolutive non-negative matrix factorization (CNMF) and its sparse version, convolutive non-negative sparse coding (CNSC), exhibit great success in speech processing. A particular limitation of the current CNMF/CNSC approaches is that the convolution ranges of the bases in learning are identical, resulting in patterns covering the same time span. This is obvious unideal as most of sequential s...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2013
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2012.2222381