Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

2 Lataukset (Pure)

Abstrakti

This paper presents the details of the Audio-Visual Scene Classification task in the DCASE 2021 Challenge (Task 1 Subtask B). The task is concerned with classification using audio and video modalities, using a dataset of synchronized recordings. This task has attracted 43 submissions from 13 different teams around the world. Among all submissions, more than half of the submitted systems have better performance than the baseline. The common techniques among the top systems are the usage of large pretrained models such as ResNet or EfficientNet which are trained for the task-specific problem. Fine-tuning, transfer learning, and data augmentation techniques are also employed to boost the performance. More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams. The best system among all achieved a logloss of 0.195 and accuracy of 93.8\%, compared to the baseline system with logloss of 0.662 and accuracy of 77.1%.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 6th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2021)
ToimittajatFrederic Font, Annamaria Mesaros, Daniel P.W. Ellis, Eduardo Fonseca, Magdalena Fuentes, Benjamin Elizalde
Sivut45-49
ISBN (elektroninen)978-84-09-36072-7
DOI - pysyväislinkit
TilaJulkaistu - 15 marrask. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaDetection and Classication of Acoustic Scenes and Events - , Espanja
Kesto: 15 marrask. 202119 marrask. 2021

Conference

ConferenceDetection and Classication of Acoustic Scenes and Events
Maa/AlueEspanja
Ajanjakso15/11/2119/11/21

Julkaisufoorumi-taso

  • Jufo-taso 0

Sormenjälki

Sukella tutkimusaiheisiin 'Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä