# End-to-End Auditory Object Recognition via Inception Nucleus

arXiv: https://arxiv.org/abs/2005.12195 · HTML: https://mlsanity.com/paper/2005.12195/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

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

- [Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks](https://mlsanity.com/paper/2107.00623/) (2021)
- [Multi-Head Deep Metric Learning Using Global and Local Representations](https://mlsanity.com/paper/2112.14327/) (2021)
- [EEG2Mel: Reconstructing Sound from Brain Responses to Music](https://mlsanity.com/paper/2207.13845/) (2022)
- [Vision-Ultrasound Robotic System based on Deep Learning for Gas and Arc Hazard Detection in Manufacturing](https://mlsanity.com/paper/2502.05500/) (2025)

## Abstract (arXiv)

Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, end-to-end classification systems in image and auditory recognition systems have been developed to learn features jointly with classification and result in improved classification accuracy. In this paper, we propose a novel end-to-end deep neural network to map the raw waveform inputs to sound class labels. Our network includes an "inception nucleus" that optimizes the size of convolutional filters on the fly that results in reducing engineering efforts dramatically. Classification results compared favorably against current state-of-the-art approaches, besting them by 10.4 percentage points on the Urbansound8k dataset. Analyses of learned representations revealed that filters in the earlier hidden layers learned wavelet-like transforms to extract features that were informative for classification.