Matches in SemOpenAlex for { <https://semopenalex.org/work/W2990614474> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W2990614474 abstract "In the era of third generation surveillance systems, it becomes more and more useful to have available a solution able to automatically detect abnormal events. The interest for audio analysis is thus growing in the last years, due to the large amount of situations where a microphone and an audio surveillance system can be profitably used by the human operator in charge of control. In this paper, we propose a method for automatically analyzing the audio stream for surveillance purposes: it is able to detect the presence of abnormal events such as screams, gun shots and broken glasses. Instead than processing directly raw data (the audio signal), the stream is represented by means of an image, namely the spectrogram, a time-frequency representation of the audio stream. In this way, we formulate the problem of audio analysis as a problem of image classification. Thus, we propose to use a Convolutional Neural Network with the following two main properties: inspired by VGG network, we employed very small kernels in convolutional layers; furthermore, we adopted a pyramidal structure in fully connected layers. These choices allow to have good generalization capabilities of the network even in presence of a not so wide dataset. The performance, computed over a standard dataset already used for benchmarking purposes in the field of audio surveillance, confirms the effectiveness of the proposed approach." @default.
- W2990614474 created "2019-12-05" @default.
- W2990614474 creator A5027593663 @default.
- W2990614474 creator A5064364003 @default.
- W2990614474 creator A5071216669 @default.
- W2990614474 date "2019-10-01" @default.
- W2990614474 modified "2023-10-14" @default.
- W2990614474 title "SoReNet: a novel deep network for audio surveillance applications" @default.
- W2990614474 cites W1501987291 @default.
- W2990614474 cites W1844944916 @default.
- W2990614474 cites W1927364623 @default.
- W2990614474 cites W2074989593 @default.
- W2990614474 cites W2107430826 @default.
- W2990614474 cites W2121562595 @default.
- W2990614474 cites W2124111503 @default.
- W2990614474 cites W2156798906 @default.
- W2990614474 cites W2162752630 @default.
- W2990614474 cites W2261950180 @default.
- W2990614474 cites W2290243020 @default.
- W2990614474 cites W2292996718 @default.
- W2990614474 cites W2556437173 @default.
- W2990614474 cites W2570915410 @default.
- W2990614474 cites W2703895418 @default.
- W2990614474 cites W2728972335 @default.
- W2990614474 cites W2729018917 @default.
- W2990614474 cites W2743986694 @default.
- W2990614474 cites W2775794021 @default.
- W2990614474 cites W2883781508 @default.
- W2990614474 cites W2893813411 @default.
- W2990614474 cites W2905548332 @default.
- W2990614474 cites W2915067382 @default.
- W2990614474 cites W2936044260 @default.
- W2990614474 cites W2962730440 @default.
- W2990614474 cites W2962999716 @default.
- W2990614474 cites W2963041956 @default.
- W2990614474 cites W2963177663 @default.
- W2990614474 cites W2963794569 @default.
- W2990614474 cites W3098357269 @default.
- W2990614474 cites W47831154 @default.
- W2990614474 cites W821549425 @default.
- W2990614474 doi "https://doi.org/10.1109/smc.2019.8914435" @default.
- W2990614474 hasPublicationYear "2019" @default.
- W2990614474 type Work @default.
- W2990614474 sameAs 2990614474 @default.
- W2990614474 citedByCount "6" @default.
- W2990614474 countsByYear W29906144742020 @default.
- W2990614474 countsByYear W29906144742021 @default.
- W2990614474 countsByYear W29906144742022 @default.
- W2990614474 countsByYear W29906144742023 @default.
- W2990614474 crossrefType "proceedings-article" @default.
- W2990614474 hasAuthorship W2990614474A5027593663 @default.
- W2990614474 hasAuthorship W2990614474A5064364003 @default.
- W2990614474 hasAuthorship W2990614474A5071216669 @default.
- W2990614474 hasConcept C28490314 @default.
- W2990614474 hasConcept C41008148 @default.
- W2990614474 hasConceptScore W2990614474C28490314 @default.
- W2990614474 hasConceptScore W2990614474C41008148 @default.
- W2990614474 hasLocation W29906144741 @default.
- W2990614474 hasOpenAccess W2990614474 @default.
- W2990614474 hasPrimaryLocation W29906144741 @default.
- W2990614474 hasRelatedWork W2066106687 @default.
- W2990614474 hasRelatedWork W2312116756 @default.
- W2990614474 hasRelatedWork W2368779261 @default.
- W2990614474 hasRelatedWork W2374918184 @default.
- W2990614474 hasRelatedWork W2778699561 @default.
- W2990614474 hasRelatedWork W2794438528 @default.
- W2990614474 hasRelatedWork W2893763841 @default.
- W2990614474 hasRelatedWork W2995996972 @default.
- W2990614474 hasRelatedWork W3128571556 @default.
- W2990614474 hasRelatedWork W4304891817 @default.
- W2990614474 isParatext "false" @default.
- W2990614474 isRetracted "false" @default.
- W2990614474 magId "2990614474" @default.
- W2990614474 workType "article" @default.