Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912278957260800 |
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| author | Mohammadi, Amirmohammad Masabarakiza, Iren'e Barnes, Ethan Carreiro, Davelle Van Dine, Alexandra Peeples, Joshua |
| author_facet | Mohammadi, Amirmohammad Masabarakiza, Iren'e Barnes, Ethan Carreiro, Davelle Van Dine, Alexandra Peeples, Joshua |
| contents | While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by which audio signals are converted into time-frequency representations and the subsequent handling of these spectrograms can significantly impact performance. This work demonstrates the performance impact of using different combinations of time-frequency features in a histogram layer time delay neural network. An optimal set of features is identified with results indicating that specific feature combinations outperform single data features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks Mohammadi, Amirmohammad Masabarakiza, Iren'e Barnes, Ethan Carreiro, Davelle Van Dine, Alexandra Peeples, Joshua Sound Machine Learning Audio and Speech Processing While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by which audio signals are converted into time-frequency representations and the subsequent handling of these spectrograms can significantly impact performance. This work demonstrates the performance impact of using different combinations of time-frequency features in a histogram layer time delay neural network. An optimal set of features is identified with results indicating that specific feature combinations outperform single data features. |
| title | Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.13881 |