# Pre-training with Synthetic Patterns for Audio

arXiv: https://arxiv.org/abs/2410.00511 · HTML: https://mlsanity.com/paper/2410.00511/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [Efficient Training of Audio Transformers with Patchout](https://mlsanity.com/paper/2110.05069/) (2021)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [SSAST: Self-Supervised Audio Spectrogram Transformer](https://mlsanity.com/paper/2110.09784/) (2021)
- [Pre-training without Natural Images](https://mlsanity.com/paper/2101.08515/) (2021)
- [Learning to See by Looking at Noise](https://mlsanity.com/paper/2106.05963/) (2021)
- [PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation](https://mlsanity.com/paper/2102.01243/) (2021)
- [HEAR: Holistic Evaluation of Audio Representations](https://mlsanity.com/paper/2203.03022/) (2022)
- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Masked Spectrogram Prediction For Self-Supervised Audio Pre-Training](https://mlsanity.com/paper/2204.12768/) (2022)
- [Audiovisual Masked Autoencoders](https://mlsanity.com/paper/2212.05922/) (2022)
- [Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems](https://mlsanity.com/paper/2011.11564/) (2020)
- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [Procedural Image Programs for Representation Learning](https://mlsanity.com/paper/2211.16412/) (2022)
- [Pre-training Vision Transformers with Formula-driven Supervised Learning](https://mlsanity.com/paper/2206.09132/) (2022)
- [Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves](https://mlsanity.com/paper/2303.01112/) (2023)
- [Benchmarking Representations for Speech, Music, and Acoustic Events](https://mlsanity.com/paper/2405.00934/) (2024)
- [Data Collection-free Masked Video Modeling](https://mlsanity.com/paper/2409.06665/) (2024)
- [Contrastive Learning from Synthetic Audio Doppelg"angers](https://mlsanity.com/paper/2406.05923/) (2024)

## Built on by (most cited first, 3 of 3)

- [From Physics to Representation: Audio Learning with Synthetic Pre-training via Procedural Generation](https://mlsanity.com/paper/2606.14791/) (2026)
- [SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models](https://mlsanity.com/paper/2606.06907/) (2026)
- [Rethinking Procedural Audio Pre-training: Source Scaling and Objective Adaptation](https://mlsanity.com/paper/2609.15067/) (2026)

## Abstract (arXiv)

In this paper, we propose to pre-train audio encoders using synthetic patterns instead of real audio data. Our proposed framework consists of two key elements. The first one is Masked Autoencoder (MAE), a self-supervised learning framework that learns from reconstructing data from randomly masked counterparts. MAEs tend to focus on low-level information such as visual patterns and regularities within data. Therefore, it is unimportant what is portrayed in the input, whether it be images, audio mel-spectrograms, or even synthetic patterns. This leads to the second key element, which is synthetic data. Synthetic data, unlike real audio, is free from privacy and licensing infringement issues. By combining MAEs and synthetic patterns, our framework enables the model to learn generalized feature representations without real data, while addressing the issues related to real audio. To evaluate the efficacy of our framework, we conduct extensive experiments across a total of 13 audio tasks and 17 synthetic datasets. The experiments provide insights into which types of synthetic patterns are effective for audio. Our results demonstrate that our framework achieves performance comparable to models pre-trained on AudioSet-2M and partially outperforms image-based pre-training methods.