# L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

arXiv: https://arxiv.org/abs/2104.05499 · HTML: https://mlsanity.com/paper/2104.05499/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 19 of 19)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [Improved Speech Enhancement with the Wave-U-Net](https://mlsanity.com/paper/1811.11307/) (2018)
- [FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing](https://mlsanity.com/paper/1909.13387/) (2019)
- [A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection](https://mlsanity.com/paper/2006.01919/) (2020)
- [Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy](https://mlsanity.com/paper/1905.00268/) (2019)
- [Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network](https://mlsanity.com/paper/1706.02291/) (2017)
- [First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation](https://mlsanity.com/paper/1910.04388/) (2019)
- [Multivariate Time Series Classification using Dilated Convolutional Neural Network](https://mlsanity.com/paper/1905.01697/) (2019)
- [SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks](https://mlsanity.com/paper/2003.01609/) (2020)
- [Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features](https://mlsanity.com/paper/1907.09884/) (2019)
- [DBNET: DOA-driven beamforming network for end-to-end farfield sound source separation](https://mlsanity.com/paper/2010.11566/) (2020)
- [Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features](https://mlsanity.com/paper/1801.09522/) (2018)
- [Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings](https://mlsanity.com/paper/2006.01708/) (2020)
- [An Iterative Graph Spectral Subtraction Method for Speech Enhancement](https://mlsanity.com/paper/2006.08497/) (2020)

## Built on by (most cited first, 9 of 9)

- [Epic-Sounds: A Large-scale Dataset of Actions That Sound](https://mlsanity.com/paper/2302.00646/) (2023)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023)
- [PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions](https://mlsanity.com/paper/2110.04176/) (2021)
- [L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment](https://mlsanity.com/paper/2202.10372/) (2022)
- [Panoramic Video Salient Object Detection with Ambisonic Audio Guidance](https://mlsanity.com/paper/2211.14419/) (2022)
- [Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation](https://mlsanity.com/paper/2204.01851/) (2022)
- [FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment](https://mlsanity.com/paper/2412.15023/) (2024)
- [Supervising Sound Localization by In-the-wild Egomotion](https://mlsanity.com/paper/2610.01388/) (2026)

## Abstract (arXiv)

The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dual-mic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and SELDNet for SELD. This report is aimed at providing all needed information to participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21 dataset, the challenge tasks and the baseline models.