Search results for: FEATURE EXTRACTION - Bridge of Knowledge

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Search results for: FEATURE EXTRACTION

Search results for: FEATURE EXTRACTION

  • Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings

    Publication

    - Year 2022

    The paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...

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  • Classification of Music Genres by Means of Listening Tests and Decision Algorithms

    The paper compares the results of audio excerpt assignment to a music genre obtained in listening tests and classification by means of decision algorithms. A short review on music description employing music styles and genres is given. Then, assumptions of listening tests to be carried out along with an online survey for assigning audio samples to selected music genres are presented. A framework for music parametrization is created...

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  • Wavelet Transform Analysis of Temperature Modulated Gas Sensor Response

    The aim of the study was to evaluate whether it is possible to extract the information about the gas concentration despite the influence of humidity. Commercial semiconductor sensor response was examined under the application of a periodic temperature change. The data was collected using measurement protocol for different concentrations of ammonia at specified levels of relative humidity. In this work we focused on the evaluation...

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  • Enabling Deeper Linguistic-based Text Analytics – Construct Development for the Criticality of Negative Service Experience

    Publication

    - IEEE Access - Year 2019

    Significant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights...

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