Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to identify whether the sound emitted from a target machine is normal...
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| creator | Koizumi, Yuma Kawaguchi, Yohei Imoto, Keisuke Nakamura, Toshiki Nikaido, Yuki Tanabe, Ryo Purohit, Harsh Suefusa, Kaori Endo, Takashi Yasuda, Masahiro Harada, Noboru |
| description | In this paper, we present the task description and discuss the results of the
DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for
Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to
identify whether the sound emitted from a target machine is normal or
anomalous. The main challenge of this task is to detect unknown anomalous
sounds under the condition that only normal sound samples have been provided as
training data. We have designed this challenge as the first benchmark of ASD
research, which includes a large-scale dataset, evaluation metrics, and a
simple baseline system. We received 117 submissions from 40 teams, and several
novel approaches have been developed as a result of this challenge. On the
basis of the analysis of the evaluation results, we discuss two new approaches
and their problems. |
| doi_str_mv | 10.48550/arxiv.2006.05822 |
| format | Article |
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DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for
Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to
identify whether the sound emitted from a target machine is normal or
anomalous. The main challenge of this task is to detect unknown anomalous
sounds under the condition that only normal sound samples have been provided as
training data. We have designed this challenge as the first benchmark of ASD
research, which includes a large-scale dataset, evaluation metrics, and a
simple baseline system. We received 117 submissions from 40 teams, and several
novel approaches have been developed as a result of this challenge. On the
basis of the analysis of the evaluation results, we discuss two new approaches
and their problems.</description><identifier>DOI: 10.48550/arxiv.2006.05822</identifier><language>eng</language><subject>Computer Science - Learning ; Computer Science - Sound ; Statistics - Machine Learning</subject><creationdate>2020-06</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2006.05822$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2006.05822$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Koizumi, Yuma</creatorcontrib><creatorcontrib>Kawaguchi, Yohei</creatorcontrib><creatorcontrib>Imoto, Keisuke</creatorcontrib><creatorcontrib>Nakamura, Toshiki</creatorcontrib><creatorcontrib>Nikaido, Yuki</creatorcontrib><creatorcontrib>Tanabe, Ryo</creatorcontrib><creatorcontrib>Purohit, Harsh</creatorcontrib><creatorcontrib>Suefusa, Kaori</creatorcontrib><creatorcontrib>Endo, Takashi</creatorcontrib><creatorcontrib>Yasuda, Masahiro</creatorcontrib><creatorcontrib>Harada, Noboru</creatorcontrib><title>Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring</title><description>In this paper, we present the task description and discuss the results of the
DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for
Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to
identify whether the sound emitted from a target machine is normal or
anomalous. The main challenge of this task is to detect unknown anomalous
sounds under the condition that only normal sound samples have been provided as
training data. We have designed this challenge as the first benchmark of ASD
research, which includes a large-scale dataset, evaluation metrics, and a
simple baseline system. We received 117 submissions from 40 teams, and several
novel approaches have been developed as a result of this challenge. On the
basis of the analysis of the evaluation results, we discuss two new approaches
and their problems.</description><subject>Computer Science - Learning</subject><subject>Computer Science - Sound</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotkM1OhDAYRbtxYUYfwJV9AbC0tIA7AuNPMhMXg2tS2o-ZRqYlLUx056MraHKTm3sXZ3EQuktInOackwfpP80lpoSImPCc0mv0XUNQ3oyTcRZLq3FtgppDWOZv6qo8bCmhBFcnOQxgj4AbGT7oI363YR7BX0wAjUvrznJwc8AHNy8UmECtzN55vJfqZCzgyllt1nfvrJmcN_Z4g656OQS4_e8Nap62TfUS7d6eX6tyF0mR0UhlHKATjLBEZJp0rEh7XQBRQiVcJH0qc8KyLtUy5UIQqnOQORO0ywC00AXboPs_7KqgHb05S__VLiraVQX7AUk9Wko</recordid><startdate>20200610</startdate><enddate>20200610</enddate><creator>Koizumi, Yuma</creator><creator>Kawaguchi, Yohei</creator><creator>Imoto, Keisuke</creator><creator>Nakamura, Toshiki</creator><creator>Nikaido, Yuki</creator><creator>Tanabe, Ryo</creator><creator>Purohit, Harsh</creator><creator>Suefusa, Kaori</creator><creator>Endo, Takashi</creator><creator>Yasuda, Masahiro</creator><creator>Harada, Noboru</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20200610</creationdate><title>Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring</title><author>Koizumi, Yuma ; Kawaguchi, Yohei ; Imoto, Keisuke ; Nakamura, Toshiki ; Nikaido, Yuki ; Tanabe, Ryo ; Purohit, Harsh ; Suefusa, Kaori ; Endo, Takashi ; Yasuda, Masahiro ; Harada, Noboru</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-c75eeb6303167d0b394fd9e0c6c1561f4a8037b4da456602d8ea8362b7eed6d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Learning</topic><topic>Computer Science - Sound</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Koizumi, Yuma</creatorcontrib><creatorcontrib>Kawaguchi, Yohei</creatorcontrib><creatorcontrib>Imoto, Keisuke</creatorcontrib><creatorcontrib>Nakamura, Toshiki</creatorcontrib><creatorcontrib>Nikaido, Yuki</creatorcontrib><creatorcontrib>Tanabe, Ryo</creatorcontrib><creatorcontrib>Purohit, Harsh</creatorcontrib><creatorcontrib>Suefusa, Kaori</creatorcontrib><creatorcontrib>Endo, Takashi</creatorcontrib><creatorcontrib>Yasuda, Masahiro</creatorcontrib><creatorcontrib>Harada, Noboru</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Koizumi, Yuma</au><au>Kawaguchi, Yohei</au><au>Imoto, Keisuke</au><au>Nakamura, Toshiki</au><au>Nikaido, Yuki</au><au>Tanabe, Ryo</au><au>Purohit, Harsh</au><au>Suefusa, Kaori</au><au>Endo, Takashi</au><au>Yasuda, Masahiro</au><au>Harada, Noboru</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring</atitle><date>2020-06-10</date><risdate>2020</risdate><abstract>In this paper, we present the task description and discuss the results of the
DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for
Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to
identify whether the sound emitted from a target machine is normal or
anomalous. The main challenge of this task is to detect unknown anomalous
sounds under the condition that only normal sound samples have been provided as
training data. We have designed this challenge as the first benchmark of ASD
research, which includes a large-scale dataset, evaluation metrics, and a
simple baseline system. We received 117 submissions from 40 teams, and several
novel approaches have been developed as a result of this challenge. On the
basis of the analysis of the evaluation results, we discuss two new approaches
and their problems.</abstract><doi>10.48550/arxiv.2006.05822</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Computer Science - Sound Statistics - Machine Learning |
| title | Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring |
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