Supervised input space scaling for non-negative matrix factorization

Driesen, Joris; hamme, Hugo Van

doi:10.1016/j.sigpro.2011.07.016

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…The performance of the ATF filtering is demonstrated in a weakly supervised word learning experiment from continuous child-directed speech (see [12][13][14]) using the CM algorithm [12]. In the experiment, the task of the learning algorithm is to discover acoustic patterns (words) in speech that co-occur with contextual labels denoting the keywords present in each utterance.…”

Section: Experimental Setup and Evaluationmentioning

confidence: 99%

Attention based temporal filtering of sensory signals for data redundancy reduction

Kakouros

Räsänen

Laine

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Since modern computational devices are required to store and process increasing amounts of data generated from various sources, efficient algorithms for identification of significant information in the data are becoming essential. Sensory recordings are one example where automatic and continuous storing and processing of large amounts of data is needed. Therefore, algorithms that can alleviate the computational load of the devices and reduce their storage requirements by removing uninformative data are important. In this work we propose a method for data reduction based on theories of human attention. The method detects temporally salient events based on the context in which they occur and retains only those sections of the input signal. The algorithm is tested as a pre-processing stage in a weakly supervised keyword learning experiment where it is shown to significantly improve the quality of the codebooks used in the pattern discovery process.

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