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Part 1: Document Description
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Citation |
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Title: |
Synthetic noise dataset |
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Identification Number: |
doi:10.21979/N9/ETJWLU |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2023-03-16 |
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Version: |
1 |
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Bibliographic Citation: |
Luo, Zhengding; Shi, Dongyuan; Gan, Woon-Seng, 2023, "Synthetic noise dataset", https://doi.org/10.21979/N9/ETJWLU, DR-NTU (Data), V1 |
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Citation |
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Title: |
Synthetic noise dataset |
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Identification Number: |
doi:10.21979/N9/ETJWLU |
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Authoring Entity: |
Luo, Zhengding (Nanyang Technological University) |
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Shi, Dongyuan (Nanyang Technological University) |
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Gan, Woon-Seng (Nanyang Technological University) |
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Software used in Production: |
Python |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Luo Zhengding |
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Depositor: |
Luo Zhengding |
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Date of Deposit: |
2023-03-16 |
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Holdings Information: |
https://doi.org/10.21979/N9/ETJWLU |
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Study Scope |
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Keywords: |
Engineering, Engineering, Synthetic noise dataset |
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Abstract: |
The synthetic noise dataset is divided into 3 subsets: 80,000 noise tracks for training, 2,000 noise tracks for validation, and remaining 2,000 noise tracks for testing. The synthetic noise tracks are generated by filtering white noise through various band-pass filters with randomly chosen center frequencies and bandwidths. Each noise track in the dataset has a 1-second duration with a sample rate of 16 kHz. |
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Kind of Data: |
Dataset |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1109/LSP.2022.3169428 |
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Bibliographic Citation: |
Luo, Z., Shi, D., & Gan, W. S. (2022). A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control Based on Deep Learning. IEEE Signal Processing Letters, 29, 1102-1106. |
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Label: |
Synthesized_Dataset.zip |
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Notes: |
application/zip |