FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2987850431> ?p ?o ?g. }

• W2987850431 endingPage "196" @default.
• W2987850431 startingPage "171" @default.
• W2987850431 abstract "In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feed-forward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized non-symmetric kernels, better results are obtained compared to common CNN architectures." @default.
• W2987850431 created "2019-11-22" @default.
• W2987850431 creator A5020891994 @default.
• W2987850431 creator A5051589781 @default.
• W2987850431 creator A5053447731 @default.
• W2987850431 creator A5080723470 @default.
• W2987850431 creator A5080979893 @default.
• W2987850431 date "2019-11-02" @default.
• W2987850431 modified "2023-10-18" @default.
• W2987850431 title "Baby Cry Detection: Deep Learning and Classical Approaches" @default.
• W2987850431 cites W1498436455 @default.
• W2987850431 cites W1501987291 @default.
• W2987850431 cites W1563939609 @default.
• W2987850431 cites W1650531274 @default.
• W2987850431 cites W1718591779 @default.
• W2987850431 cites W1814414116 @default.
• W2987850431 cites W1972567154 @default.
• W2987850431 cites W1974973544 @default.
• W2987850431 cites W1977144816 @default.
• W2987850431 cites W1979173241 @default.
• W2987850431 cites W1984733939 @default.
• W2987850431 cites W1987426045 @default.
• W2987850431 cites W1991453734 @default.
• W2987850431 cites W2005708641 @default.
• W2987850431 cites W2007645178 @default.
• W2987850431 cites W2016983264 @default.
• W2987850431 cites W2029336857 @default.
• W2987850431 cites W2035302738 @default.
• W2987850431 cites W2038593237 @default.
• W2987850431 cites W2042081239 @default.
• W2987850431 cites W2053661818 @default.
• W2987850431 cites W2057457804 @default.
• W2987850431 cites W2059631048 @default.
• W2987850431 cites W2059652044 @default.
• W2987850431 cites W2060041168 @default.
• W2987850431 cites W2064675550 @default.
• W2987850431 cites W2065388446 @default.
• W2987850431 cites W2083001895 @default.
• W2987850431 cites W2084044763 @default.
• W2987850431 cites W2089390156 @default.
• W2987850431 cites W2103235956 @default.
• W2987850431 cites W2126326123 @default.
• W2987850431 cites W2131774270 @default.
• W2987850431 cites W2133824856 @default.
• W2987850431 cites W2137300673 @default.
• W2987850431 cites W2137644731 @default.
• W2987850431 cites W2144499799 @default.
• W2987850431 cites W2151305614 @default.
• W2987850431 cites W2152110400 @default.
• W2987850431 cites W2158973647 @default.
• W2987850431 cites W2194775991 @default.
• W2987850431 cites W2526050071 @default.
• W2987850431 cites W2526852292 @default.
• W2987850431 cites W2591013610 @default.
• W2987850431 cites W2741049562 @default.
• W2987850431 cites W2771725120 @default.
• W2987850431 cites W2782735517 @default.
• W2987850431 cites W2785943966 @default.
• W2987850431 cites W2808952658 @default.
• W2987850431 cites W2883466965 @default.
• W2987850431 cites W2889411717 @default.
• W2987850431 cites W2889462968 @default.
• W2987850431 cites W2899190501 @default.
• W2987850431 cites W2902761001 @default.
• W2987850431 cites W2916145674 @default.
• W2987850431 cites W2938934205 @default.
• W2987850431 cites W2963500860 @default.
• W2987850431 cites W3121328487 @default.
• W2987850431 cites W4242960084 @default.
• W2987850431 cites W4254193920 @default.
• W2987850431 doi "https://doi.org/10.1007/978-3-030-31764-5_7" @default.
• W2987850431 hasPublicationYear "2019" @default.
• W2987850431 type Work @default.
• W2987850431 sameAs 2987850431 @default.
• W2987850431 citedByCount "12" @default.
• W2987850431 countsByYear W29878504312020 @default.
• W2987850431 countsByYear W29878504312021 @default.
• W2987850431 countsByYear W29878504312022 @default.
• W2987850431 countsByYear W29878504312023 @default.
• W2987850431 crossrefType "book-chapter" @default.
• W2987850431 hasAuthorship W2987850431A5020891994 @default.
• W2987850431 hasAuthorship W2987850431A5051589781 @default.
• W2987850431 hasAuthorship W2987850431A5053447731 @default.
• W2987850431 hasAuthorship W2987850431A5080723470 @default.
• W2987850431 hasAuthorship W2987850431A5080979893 @default.
• W2987850431 hasBestOaLocation W29878504313 @default.
• W2987850431 hasConcept C108583219 @default.
• W2987850431 hasConcept C119857082 @default.
• W2987850431 hasConcept C12267149 @default.
• W2987850431 hasConcept C147168706 @default.
• W2987850431 hasConcept C153180895 @default.
• W2987850431 hasConcept C154945302 @default.
• W2987850431 hasConcept C2984842247 @default.
• W2987850431 hasConcept C41008148 @default.
• W2987850431 hasConcept C50644808 @default.
• W2987850431 hasConcept C81363708 @default.
• W2987850431 hasConceptScore W2987850431C108583219 @default.
• W2987850431 hasConceptScore W2987850431C119857082 @default.

• next