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- W2899551440 abstract "A convolutional neural network architecture, trained with a semi-supervised strategy, is proposed for speech/music classification (SMC) and segmentation (SMS). It is compared to baseline machine learning algorithms on three SMC corpora and demonstrates superior performances, associated to perfect media-level speech recall scores. Evaluation corpora include speech-over-music segments with durations varying between 3 and 30 seconds. Early SMS results are presented. Segmentation errors are associated to musical genres not covered in the training database, and/or with close to speech acoustic properties. These experiments are aimed to help the design of novel speech/music annotated resources and evaluation protocols, suited to TV and radio stream indexation." @default.
- W2899551440 created "2018-11-16" @default.
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- W2899551440 date "2017-04-23" @default.
- W2899551440 modified "2023-09-23" @default.
- W2899551440 title "Investigating the Use of Semi-Supervised Convolutional Neural Network Models for Speech/Music Classification and Segmentation" @default.
- W2899551440 hasPublicationYear "2017" @default.
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