Temporal Sub-sampling of Audio Feature Sequences for Automated Audio Captioning

Khoa Nguyen, Konstantinos Drossos, Tuomas Virtanen

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

Abstrakti

Audio captioning is the task of automatically creating a textual description for the contents of a general audio signal. Typical audio captioning methods rely on deep neural networks (DNNs), where the target of the DNN is to map the input audio sequence to an output sequence of words, i.e. the caption. Though, the length of the textual description is considerably less than the length of the audio signal, for example 10 words versus some thousands of audio feature vectors. This clearly indicates that an output word corresponds to multiple input feature vectors. In this work we present an approach that focuses on explicitly taking advantage of this difference of lengths between sequences, by applying a temporal sub-sampling to the audio input sequence. We employ a sequence-to-sequence method, which uses a fixed-length vector as an output from the encoder, and we apply temporal sub-sampling between the RNNs of the encoder. We evaluate the benefit of our approach by employing the freely available dataset Clotho and we evaluate the impact of different factors of temporal sub-sampling. Our results show an improvement to all considered metrics.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the Fifth Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2020)
ToimittajatNobutaka Ono, Noboru Harada, Yohei Kawaguchi, Annamaria Mesaros, Keisuke Imoto, Yuma Koizumi, Tatsuya Komatsu
Sivut110-114
ISBN (elektroninen)978-4-600-00566-5
TilaJulkaistu - 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaWorkshop on Detection and Classification of Acoustic Scenes and Events - Tokyo, Japani
Kesto: 2 marrask. 20203 marrask. 2020
http://dcase.community/workshop2020/

Workshop

WorkshopWorkshop on Detection and Classification of Acoustic Scenes and Events
LyhennettäDCASE 2020
Maa/AlueJapani
KaupunkiTokyo
Ajanjakso2/11/203/11/20
www-osoite

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