Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint
نویسندگان
چکیده
منابع مشابه
Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint, ...
متن کاملConvolutive Non-negative Matrix Factorisation with Sparseness Constraint
Discovering a parsimonious representation that reflects the structure of audio is a requirement of many machine learning and signal processing methods. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. We present an extension to NMF that is convolutive and forces a sparseness const...
متن کاملDiscovering Convolutive Speech Phones Using Sparseness and Non-negativity
Abstract Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF). Here, we present a convolutive NMF algorithm that includes a sparseness constraint on the activations and has multiplicative updates. In combination w...
متن کاملDiscovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Nonnegative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness cons...
متن کاملDiscovering hierarchical speech features using convolutional non-negative matrix factorization
Discovering a representation that reflects the structure of a dataset is a first step for many inference and learning methods. This paper aims at finding a hierarchy of localized speech features that can be interpreted as parts. Non-negative matrix factorization (NMF) has been proposed recently for the discovery of parts-based localized additive representations. Here, I propose a variant of thi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2008
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2008.01.033