ASSESSMENT OF HUMAN AND MACHINE PERFORMANCE IN ACOUSTIC SCENE CLASSIFICATION: DCASE 2016 CASE STUDY

被引：0

作者：

Mesaros, Annamaria ^{[1
]}

Heittola, Toni ^{[1
]}

Virtanen, Tuomas ^{[1
]}

机构：

[1] Tampere Univ Technol, Dept Signal Proc, Korkeakoulunkatu 10, Tampere 33720, Finland

来源：

2017 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA) | 2017年

基金：

欧洲研究理事会;

关键词：

acoustic scene classification; machine learning; human performance; listening experiment;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Human and machine performance in acoustic scene classification is examined through a parallel experiment using TUT Acoustic Scenes 2016 dataset. The machine learning perspective is presented based on the systems submitted for the 2016 challenge on Detection and Classification of Acoustic Scenes and Events. The human performance, assessed through a listening experiment, was found to be significantly lower than machine performance. Test subjects exhibited different behavior throughout the experiment, leading to significant differences in performance between groups of subjects. An expert listener trained for the task obtained similar accuracy to the average of submitted systems, comparable also to previous studies of human abilities in recognizing everyday acoustic scenes.

引用

页码：319 / 323

页数：5

共 50 条

[41] Combining human input with machine learning: A case study
Upshall, Michael
LEARNED PUBLISHING, 2018, 31 (02) : 157 - 160
[42] Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification
Petrovska, Biserka
Zdravevski, Eftim
Lameski, Petre
Corizzo, Roberto
Stajduhar, Ivan
Lerga, Jonatan
SENSORS, 2020, 20 (14) : 1 - 22
[43] Study of Engineered Features and Learning Features in Machine Learning - A Case Study in Document Classification
Sen, Arpan
Ghosh, Shrestha
Kundu, Debottam
Sarkar, Debleena
Sil, Jaya
INTELLIGENT HUMAN COMPUTER INTERACTION, IHCI 2016, 2017, 10127 : 161 - 172
[44] The assessment of a road “acoustic capacity” methodology and application to a real case of study
Lupi M.
Conte D.
Farina A.
Transportation Engineering, 2023, 13
[45] Human Resource Selection Based on Performance Classification Using Weighted Support Vector Machine
Wang, Qiangwei
Li, Boyang
Hu, Jinglu
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2009, 13 (04) : 407 - 415
[46] Training machine classifiers to match the performance of human listeners in a natural vowel classification task
Hunke, M
Holton, T
ICSLP 96 - FOURTH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, PROCEEDINGS, VOLS 1-4, 1996, : 574 - 577
[47] IMPROVING THE PERFORMANCE OF THE SUPPORT VECTOR MACHINE IN INSURANCE RISK CLASSIFICATION A Comparitive Study
Duma, Mlungisi
Twala, Bhekisipho
Marwala, Tshilidzi
Nelwamondo, Fulufhelo V.
NCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NEURAL COMPUTATION THEORY AND APPLICATIONS, 2011, : 340 - 346
[48] Comparative study on classification performance between support vector machine and logistic regression
Musa, Abdallah Bashir
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2013, 4 (01) : 13 - 24
[49] Comparative study on classification performance between support vector machine and logistic regression
Abdallah Bashir Musa
International Journal of Machine Learning and Cybernetics, 2013, 4 : 13 - 24
[50] A Case Study of Applying the Classification Task for Students' Performance Prediction
Guerra, M. S.
Asseiss Neto, H.
Oliveira, S. A.
IEEE LATIN AMERICA TRANSACTIONS, 2018, 16 (01) : 172 - 177

← 1 2 3 4 5 →