# End-to-end Binaural Sound Localisation from the Raw Waveform

arXiv: https://arxiv.org/abs/1904.01916 · HTML: https://mlsanity.com/paper/1904.01916/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Sound Localization by Self-Supervised Time Delay Estimation](https://mlsanity.com/paper/2204.12489/) (2022)
- [Sound Localization from Motion: Jointly Learning Sound Direction and Camera Rotation](https://mlsanity.com/paper/2303.11329/) (2023)
- [Iterative Sound Source Localization for Unknown Number of Sources](https://mlsanity.com/paper/2206.12273/) (2022)
- [Feature Aggregation in Joint Sound Classification and Localization Neural Networks](https://mlsanity.com/paper/2310.19063/) (2023)
- [Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization](https://mlsanity.com/paper/2511.13487/) (2025)

## Abstract (arXiv)

A novel end-to-end binaural sound localisation approach is proposed which estimates the azimuth of a sound source directly from the waveform. Instead of employing hand-crafted features commonly employed for binaural sound localisation, such as the interaural time and level difference, our end-to-end system approach uses a convolutional neural network (CNN) to extract specific features from the waveform that are suitable for localisation. Two systems are proposed which differ in the initial frequency analysis stage. The first system is auditory-inspired and makes use of a gammatone filtering layer, while the second system is fully data-driven and exploits a trainable convolutional layer to perform frequency analysis. In both systems, a set of dedicated convolutional kernels are then employed to search for specific localisation cues, which are coupled with a localisation stage using fully connected layers. Localisation experiments using binaural simulation in both anechoic and reverberant environments show that the proposed systems outperform a state-of-the-art deep neural network system. Furthermore, our investigation of the frequency analysis stage in the second system suggests that the CNN is able to exploit different frequency bands for localisation according to the characteristics of the reverberant environment.