# CNN depth analysis with different channel inputs for Acoustic Scene Classification

arXiv: https://arxiv.org/abs/1906.04591 · HTML: https://mlsanity.com/paper/1906.04591/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Learning Nigerian accent embeddings from speech: preliminary results based on SautiDB-Naija corpus](https://mlsanity.com/paper/2112.06199/) (2021)
- [Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification](https://mlsanity.com/paper/2107.13180/) (2021)
- [Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint](https://mlsanity.com/paper/2107.14658/) (2021)
- [DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations](https://mlsanity.com/paper/2206.08007/) (2022)

## Abstract (arXiv)

Acoustic scene classification (ASC) has been approached in the last years using deep learning techniques such as convolutional neural networks or recurrent neural networks. Many state-of-the-art solutions are based on image classification frameworks and, as such, a 2D representation of the audio signal is considered for training these networks. Finding the most suitable audio representation is still a research area of interest. In this paper, different log-Mel representations and combinations are analyzed. Experiments show that the best results are obtained using the harmonic and percussive components plus the difference between left and right stereo channels, (L-R). On the other hand, it is a common strategy to ensemble different models in order to increase the final accuracy. Even though averaging different model predictions is a common choice, an exhaustive analysis of different ensemble techniques has not been presented in ASC problems. In this paper, geometric and arithmetic mean plus the Ordered Weighted Averaging (OWA) operator are studied as aggregation operators for the output of the different models of the ensemble. Finally, the work carried out in this paper is highly oriented towards real-time implementations. In this context, as the number of applications for audio classification on edge devices is increasing exponentially, we also analyze different network depths and efficient solutions for aggregating ensemble predictions.