Applying the FN-Corrector to improve the quality of audio event classifica
The paper deals with the problem of acoustic events classification which is actively applied to the problems of a safe city, smart home, IoT devices, and for the detection of industrial accidence. A solution to improve the accuracy of classifiers without changing their structure and collecting add...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2022-08-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | https://ntv.ifmo.ru/file/article/21352.pdf |
| _version_ | 1817999238213140480 |
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| author | Alexander M. Golubkov Evgeny V. Shuranov |
| author_facet | Alexander M. Golubkov Evgeny V. Shuranov |
| author_sort | Alexander M. Golubkov |
| collection | DOAJ |
| description | The paper deals with the problem of acoustic events classification which is actively applied to the problems of a safe city,
smart home, IoT devices, and for the detection of industrial accidence. A solution to improve the accuracy of classifiers
without changing their structure and collecting additional data is proposed. The main data source for the experiments
was the TUT Urban Acoustic Scenes 2018, Development Dataset. The paper presents the way to increase the accuracy
of audio event classification by using the FN-corrector. The FN-corrector is a linear two-stage classifier performing the
transformation of the feature space into a linearly separable space and the linear separation of one class from another.
If a corrector is applied, the responses of the original classifier generate four classes: positive (P), negative (N), false
positive (FP), and false negative (FN). As a result, it becomes possible to train two types of correctors: the FP-corrector
separating positive and false positive classifier responses, and the FN-corrector separating negative and false negative
classifier responses. In the experiments, the VGGish convolutional neural network was used as the initial classifier. The
audio signal is converted into a spectrogram and is fed to the input of the neural network which forms the spectrogram
feature description and performs a classification. As an example, two ”confused“ classes are selected to demonstrate the
increase in classification accuracy. Using the feature description of audio recordings of these classes, an FN-corrector was
built, trained and connected to the original classifier. The response from the classifier, as well as the feature description,
has been passed to the corrector input. Next, the corrector translated the feature space into a new basis (into a linearly
separable space) and classified the classifier answer responding to the question whether the original classifier makes
a mistake on such a feature vector or not. If the original classifier made a mistake, then his answer is changed by the
corrector to the opposite, otherwise the answer remains the same. The results of the experiments demonstrated a decrease
in the level of class confusion and, accordingly, an increase in the accuracy of the original classifier without changing
its structure and without collecting an additional data set. The results obtained can be used on IoT devices that have
significant limitations on the size of the models used, as well as in solving the problems of domain adaptation which is
relevant in audio analytics. |
| first_indexed | 2024-04-14T03:06:02Z |
| format | Article |
| id | doaj.art-50bd429ba77c4acb9a7285b322828de8 |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-04-14T03:06:02Z |
| publishDate | 2022-08-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-50bd429ba77c4acb9a7285b322828de82022-12-22T02:15:44ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732022-08-0122470871510.17586/2226-1494-2022-22-4-708-715Applying the FN-Corrector to improve the quality of audio event classificaAlexander M. Golubkov0https://orcid.org/0000-0002-8330-1823Evgeny V. Shuranov1https://orcid.org/0000-0003-0977-5075PhD, Assistant, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, 197022, Russian Federation; Senior Machine Learning Engineer, Huawei, Moscow, 123007, Russian Federation, sc 57190975154PhD, Associate Professor, ITMO University, 197101, Russian Federation; Head of Laboratory, Huawei, Moscow, 123007, Russian Federation, sc 57190970283The paper deals with the problem of acoustic events classification which is actively applied to the problems of a safe city, smart home, IoT devices, and for the detection of industrial accidence. A solution to improve the accuracy of classifiers without changing their structure and collecting additional data is proposed. The main data source for the experiments was the TUT Urban Acoustic Scenes 2018, Development Dataset. The paper presents the way to increase the accuracy of audio event classification by using the FN-corrector. The FN-corrector is a linear two-stage classifier performing the transformation of the feature space into a linearly separable space and the linear separation of one class from another. If a corrector is applied, the responses of the original classifier generate four classes: positive (P), negative (N), false positive (FP), and false negative (FN). As a result, it becomes possible to train two types of correctors: the FP-corrector separating positive and false positive classifier responses, and the FN-corrector separating negative and false negative classifier responses. In the experiments, the VGGish convolutional neural network was used as the initial classifier. The audio signal is converted into a spectrogram and is fed to the input of the neural network which forms the spectrogram feature description and performs a classification. As an example, two ”confused“ classes are selected to demonstrate the increase in classification accuracy. Using the feature description of audio recordings of these classes, an FN-corrector was built, trained and connected to the original classifier. The response from the classifier, as well as the feature description, has been passed to the corrector input. Next, the corrector translated the feature space into a new basis (into a linearly separable space) and classified the classifier answer responding to the question whether the original classifier makes a mistake on such a feature vector or not. If the original classifier made a mistake, then his answer is changed by the corrector to the opposite, otherwise the answer remains the same. The results of the experiments demonstrated a decrease in the level of class confusion and, accordingly, an increase in the accuracy of the original classifier without changing its structure and without collecting an additional data set. The results obtained can be used on IoT devices that have significant limitations on the size of the models used, as well as in solving the problems of domain adaptation which is relevant in audio analytics.https://ntv.ifmo.ru/file/article/21352.pdfacoustic event detectionaudio processingfn-correctorfalse negative correctordspcnnconvolutional neural network |
| spellingShingle | Alexander M. Golubkov Evgeny V. Shuranov Applying the FN-Corrector to improve the quality of audio event classifica Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki acoustic event detection audio processing fn-corrector false negative corrector dsp cnn convolutional neural network |
| title | Applying the FN-Corrector to improve the quality of audio event classifica |
| title_full | Applying the FN-Corrector to improve the quality of audio event classifica |
| title_fullStr | Applying the FN-Corrector to improve the quality of audio event classifica |
| title_full_unstemmed | Applying the FN-Corrector to improve the quality of audio event classifica |
| title_short | Applying the FN-Corrector to improve the quality of audio event classifica |
| title_sort | applying the fn corrector to improve the quality of audio event classifica |
| topic | acoustic event detection audio processing fn-corrector false negative corrector dsp cnn convolutional neural network |
| url | https://ntv.ifmo.ru/file/article/21352.pdf |
| work_keys_str_mv | AT alexandermgolubkov applyingthefncorrectortoimprovethequalityofaudioeventclassifica AT evgenyvshuranov applyingthefncorrectortoimprovethequalityofaudioeventclassifica |