An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance
Audio classification tasks like speech recognition and acoustic scene analysis require substantial labeled data, which is expensive. This work explores active learning to reduce annotation costs for a sound classification problem with rare target classes where existing datasets are insufficient. A d...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-12-01
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| Series: | International Journal of Engineering Business Management |
| Online Access: | https://doi.org/10.1177/18479790231223631 |
| _version_ | 1797378403641327616 |
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| author | Mohamed Salama |
| author_facet | Mohamed Salama |
| author_sort | Mohamed Salama |
| collection | DOAJ |
| description | Audio classification tasks like speech recognition and acoustic scene analysis require substantial labeled data, which is expensive. This work explores active learning to reduce annotation costs for a sound classification problem with rare target classes where existing datasets are insufficient. A deep convolutional recurrent neural network extracts spectro-temporal features and makes predictions. An uncertainty sampling strategy queries the most uncertain samples for manual labeling by experts and non-experts. A new alternating confidence sampling strategy and two other certainty-based strategies are proposed and evaluated. Experiments show significantly higher accuracy than passive learning baselines with the same labeling budget. Active learning generalizes well in a qualitative analysis of 20,000 unlabeled recordings. Overall, active learning with a novel sampling strategy minimizes the need for expensive labeled data in audio classification, successfully leveraging unlabeled data to improve accuracy with minimal supervision. |
| first_indexed | 2024-03-08T20:07:22Z |
| format | Article |
| id | doaj.art-bf248fcbf4e248159b9ddd046f279be2 |
| institution | Directory Open Access Journal |
| issn | 1847-9790 |
| language | English |
| last_indexed | 2024-03-08T20:07:22Z |
| publishDate | 2023-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Engineering Business Management |
| spelling | doaj.art-bf248fcbf4e248159b9ddd046f279be22023-12-23T10:03:19ZengSAGE PublishingInternational Journal of Engineering Business Management1847-97902023-12-011510.1177/18479790231223631An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instanceMohamed SalamaAudio classification tasks like speech recognition and acoustic scene analysis require substantial labeled data, which is expensive. This work explores active learning to reduce annotation costs for a sound classification problem with rare target classes where existing datasets are insufficient. A deep convolutional recurrent neural network extracts spectro-temporal features and makes predictions. An uncertainty sampling strategy queries the most uncertain samples for manual labeling by experts and non-experts. A new alternating confidence sampling strategy and two other certainty-based strategies are proposed and evaluated. Experiments show significantly higher accuracy than passive learning baselines with the same labeling budget. Active learning generalizes well in a qualitative analysis of 20,000 unlabeled recordings. Overall, active learning with a novel sampling strategy minimizes the need for expensive labeled data in audio classification, successfully leveraging unlabeled data to improve accuracy with minimal supervision.https://doi.org/10.1177/18479790231223631 |
| spellingShingle | Mohamed Salama An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance International Journal of Engineering Business Management |
| title | An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance |
| title_full | An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance |
| title_fullStr | An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance |
| title_full_unstemmed | An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance |
| title_short | An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance |
| title_sort | innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance |
| url | https://doi.org/10.1177/18479790231223631 |
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