# A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification

arXiv: https://arxiv.org/abs/2107.01461 · HTML: https://mlsanity.com/paper/2107.01461/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask](https://mlsanity.com/paper/1905.01067/) (2019)
- [Proving the Lottery Ticket Hypothesis: Pruning is All You Need](https://mlsanity.com/paper/2002.00585/) (2020)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [Slow-Fast Auditory Streams For Audio Recognition](https://mlsanity.com/paper/2103.03516/) (2021)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [Multi-task Language Modeling for Improving Speech Recognition of Rare Words](https://mlsanity.com/paper/2011.11715/) (2020)
- [Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement](https://mlsanity.com/paper/2003.13917/) (2020)
- [Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems](https://mlsanity.com/paper/2105.13734/) (2021)
- [Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation](https://mlsanity.com/paper/2007.08389/) (2020)
- [Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling](https://mlsanity.com/paper/1907.06639/) (2019)
- [A Two-Stage Approach to Device-Robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.01447/) (2020)
- [Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification](https://mlsanity.com/paper/2008.00110/) (2020)
- [Unsupervised Multi-Target Domain Adaptation for Acoustic Scene Classification](https://mlsanity.com/paper/2105.10340/) (2021)
- [An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances](https://mlsanity.com/paper/2008.00107/) (2020)
- [Multi-Scale Temporal Convolution Network for Classroom Voice Detection](https://mlsanity.com/paper/2105.14717/) (2021)

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

- [A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning](https://mlsanity.com/paper/2202.08509/) (2022)
- [DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations](https://mlsanity.com/paper/2206.08007/) (2022)
- [Improving Acoustic Scene Classification with City Features](https://mlsanity.com/paper/2503.16862/) (2025)
- [Compressing Quaternion Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2510.21388/) (2025)

## Abstract (arXiv)

We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC). Specifically, we tackle the ASC task in a low-resource environment leveraging a recently proposed advanced neural network pruning mechanism, namely Lottery Ticket Hypothesis (LTH), to find a sub-network neural model associated with a small amount non-zero model parameters. The effectiveness of LTH for low-complexity acoustic modeling is assessed by investigating various data augmentation and compression schemes, and we report an efficient joint framework for low-complexity multi-device ASC, called Acoustic Lottery. Acoustic Lottery could compress an ASC model up to 1/104 and attain a superior performance (validation accuracy of 79.4% and Log loss of 0.64) compared to its not compressed seed model. All results reported in this work are based on a joint effort of four groups, namely GT-USTC-UKE-Tencent, aiming to address the "Low-Complexity Acoustic Scene Classification (ASC) with Multiple Devices" in the DCASE 2021 Challenge Task 1a.