# Utilizing Domain Knowledge in End-to-End Audio Processing

arXiv: https://arxiv.org/abs/1712.00254 · HTML: https://mlsanity.com/paper/1712.00254/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 1 of 1)

- [End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network](https://mlsanity.com/paper/2204.11479/) (2022)

## Abstract (arXiv)

End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations. In this paper we present preliminary work that shows the feasibility of training the first layers of a deep convolutional neural network (CNN) model to learn the commonly-used log-scaled mel-spectrogram transformation. Secondly, we demonstrate that upon initializing the first layers of an end-to-end CNN classifier with the learned transformation, convergence and performance on the ESC-50 environmental sound classification dataset are similar to a CNN-based model trained on the highly pre-processed log-scaled mel-spectrogram features.