An Enhanced Approach for Environmental Sound Classification Using Multi-window Augmentation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36670-3_6
Reference8 articles.
1. Pham, L., Phan, H., Nguyen, T., Palaniappan, R., Mertins, A., & McLoughlin, I. (2021). Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework. Digital Signal Processing, 110, 102943.
2. Xie, J., Hu, K., Zhu, M., & Guo, Y. (2020). Bioacoustic signal classification in continuous recordings: Syllable-segmentation vs sliding-window. Expert Systems with Applications, 152, 113390.
3. Li, S., Yao, Y., Hu, J., Liu, G., Yao, X., & Hu, J. (2018). An ensemble stacked convolutional neural network model for environmental event sound recognition. Applied Sciences, 8, 1152.
4. Zhang, T., Feng, G., Liang, J., & An, T. (2021). Acoustic scene classification based on Mel spectrogram decomposition and model merging. Applied Acoustics, 182, 108258.
5. Jothimani, S., & Premalatha, K. (2022). MFF-SAug: Multi feature fusion with spectrogram augmentation of speech emotion recognition using convolution neural network. Chaos, Solitons & Fractals, 162, 112512.
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