# Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning

arXiv: https://arxiv.org/abs/1603.05824 · HTML: https://mlsanity.com/paper/1603.05824/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2016.

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

- [Explainable AI for Time Series via Virtual Inspection Layers](https://mlsanity.com/paper/2303.06365/) (2023)
- [InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries](https://mlsanity.com/paper/2409.19689/) (2024)
- [DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning](https://mlsanity.com/paper/2501.18423/) (2025)
- [Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms](https://mlsanity.com/paper/2307.10773/) (2023)

## Abstract (arXiv)

Recognizing acoustic events is an intricate problem for a machine and an emerging field of research. Deep neural networks achieve convincing results and are currently the state-of-the-art approach for many tasks. One advantage is their implicit feature learning, opposite to an explicit feature extraction of the input signal. In this work, we analyzed whether more discriminative features can be learned from either the time-domain or the frequency-domain representation of the audio signal. For this purpose, we trained multiple deep networks with different architectures on the Freiburg-106 and ESC-10 datasets. Our results show that feature learning from the frequency domain is superior to the time domain. Moreover, additionally using convolution and pooling layers, to explore local structures of the audio signal, significantly improves the recognition performance and achieves state-of-the-art results.