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TUTCRIS

Polyphonic Sound Event Detection Using Multi Label Deep Neural Networks

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2015 International Joint Conference on Neural Networks (IJCNN)
KustantajaIEEE
ISBN (painettu)978-1-4799-1959-8
DOI - pysyväislinkit
TilaJulkaistu - heinäkuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceINTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS
Ajanjakso1/01/00 → …

Tiivistelmä

In this paper, the use of multi label neural networks are proposed for detection of temporally overlapping sound events in realistic environments. Real-life sound recordings typically have many overlapping sound events, making it hard to recognize each event with the standard sound event detection methods. Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi label classification in this work. The model is evaluated with recordings from realistic everyday environments and the obtained overall accuracy is 63.8%. The method is compared against a state-of-the-art method using non-negative matrix factorization as a pre-processing stage and hidden Markov models as a classifier. The proposed method improves the accuracy by 19% percentage points overall.

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