Datasets:

renumics
/

dcase23-task2-enriched

Tasks:

Audio Classification

Size:

ArXiv:

Tags:

anomaly detection

anomalous sound detection

acoustic condition monitoring

sound machine fault diagnosis

machine learning

unsupervised learning

License:

Dataset card Files Files and versions Community

Syoy commited on May 9, 2023

Commit

f0e2950

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1 Parent(s): 1444a6f

updated readme

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@@ -168,8 +168,6 @@ The length of each audio file is 10 seconds.
 ### Data Fields
-[//]: # (todo: add new data fields)
 - `audio`: an `datasets.Audio`
 - `path`: a string representing the path of the audio file inside the _tar.gz._-archive.
 - `section`: an integer representing the section, see [Definition](#Description)
@@ -178,8 +176,8 @@ The length of each audio file is 10 seconds.
 - `domain`: an integer whose value may be either _0_, indicating that the audio sample is from the _source_ domain, _1_, indicating that the audio sample is from the _target_.
 - `class`: an integer as class label.
 - `label`: an integer whose value may be either _0_, indicating that the audio sample is _normal_, _1_, indicating that the audio sample contains an _anomaly_.
-- '*_lof_anomaly': an integer as anomaly indicator. The anomaly prediction is computed with the [Local Outlier Factor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) algorithm based on the "*"-dataset.
-- '*_lof_anomaly_score': a float as anomaly score. The anomaly score is computed with the [Local Outlier Factor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) algorithm based on the "*"-dataset.
 - `embeddings_ast-finetuned-audioset-10-10-0.4593`: an `datasets.Sequence(Value("float32"), shape=(1, 768))` representing audio embeddings that are generated with an [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTFeatureExtractor).
 ### Data Splits

 ### Data Fields
 - `audio`: an `datasets.Audio`
 - `path`: a string representing the path of the audio file inside the _tar.gz._-archive.
 - `section`: an integer representing the section, see [Definition](#Description)
 - `domain`: an integer whose value may be either _0_, indicating that the audio sample is from the _source_ domain, _1_, indicating that the audio sample is from the _target_.
 - `class`: an integer as class label.
 - `label`: an integer whose value may be either _0_, indicating that the audio sample is _normal_, _1_, indicating that the audio sample contains an _anomaly_.
+- '[*]_lof_anomaly': an integer as anomaly indicator. The anomaly prediction is computed with the [Local Outlier Factor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) algorithm based on the "[*]"-dataset.
+- '[*]_lof_anomaly_score': a float as anomaly score. The anomaly score is computed with the [Local Outlier Factor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) algorithm based on the "[*]"-dataset.
 - `embeddings_ast-finetuned-audioset-10-10-0.4593`: an `datasets.Sequence(Value("float32"), shape=(1, 768))` representing audio embeddings that are generated with an [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTFeatureExtractor).
 ### Data Splits