# ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition

arXiv: https://arxiv.org/abs/2106.01621 · HTML: https://mlsanity.com/paper/2106.01621/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2021.

## Built on by (most cited first, 29 of 29)

- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [A-JEPA: Joint-Embedding Predictive Architecture Can Listen](https://mlsanity.com/paper/2311.15830/) (2023)
- [HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2401.05698/) (2024)
- [Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation](https://mlsanity.com/paper/2211.04772/) (2022)
- [Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks](https://mlsanity.com/paper/2110.07313/) (2021)
- [Zorro: the masked multimodal transformer](https://mlsanity.com/paper/2301.09595/) (2023)
- [A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition](https://mlsanity.com/paper/2111.02172/) (2021)
- [CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2207.10644/) (2022)
- [Multimodal Emotion Recognition using Audio-Video Transformer Fusion with Cross Attention](https://mlsanity.com/paper/2407.18552/) (2024)
- [E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks](https://mlsanity.com/paper/2305.18665/) (2023)
- [Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block](https://mlsanity.com/paper/2211.02940/) (2022)
- [Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models](https://mlsanity.com/paper/2310.15648/) (2023)
- [MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition](https://mlsanity.com/paper/2404.18327/) (2024)
- [TACNET: Temporal Audio Source Counting Network](https://mlsanity.com/paper/2311.02369/) (2023)
- [OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities](https://mlsanity.com/paper/2409.11059/) (2024)
- [A Survey of Recent Advances and Challenges in Deep Audio-Visual Correlation Learning](https://mlsanity.com/paper/2412.00049/) (2024)
- [Audio classification with Dilated Convolution with Learnable Spacings](https://mlsanity.com/paper/2309.13972/) (2023)
- [VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition](https://mlsanity.com/paper/2401.05531/) (2024)
- [AudioMosaic: Contrastive Masked Audio Representation Learning](https://mlsanity.com/paper/2605.14231/) (2026)
- [Recent Advances and Challenges in Deep Audio-Visual Correlation Learning](https://mlsanity.com/paper/2202.13673/) (2022)
- [End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network](https://mlsanity.com/paper/2204.11479/) (2022)
- [An investigation on selecting audio pre-trained models for audio captioning](https://mlsanity.com/paper/2208.06127/) (2022)
- [e-Inu: Simulating A Quadruped Robot With Emotional Sentience](https://mlsanity.com/paper/2301.00964/) (2023)
- [Mixer is more than just a model](https://mlsanity.com/paper/2402.18007/) (2024)
- [Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models](https://mlsanity.com/paper/2503.11373/) (2025)
- [Compressing Quaternion Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2510.21388/) (2025)

## Abstract (arXiv)

Audio pattern recognition (APR) is an important research topic and can be applied to several fields related to our lives. Therefore, accurate and efficient APR systems need to be developed as they are useful in real applications. In this paper, we propose a new convolutional neural network (CNN) architecture and a method for improving the inference speed of CNN-based systems for APR tasks. Moreover, using the proposed method, we can improve the performance of our systems, as confirmed in experiments conducted on four audio datasets. In addition, we investigate the impact of data augmentation techniques and transfer learning on the performance of our systems. Our best system achieves a mean average precision (mAP) of 0.450 on the AudioSet dataset. Although this value is less than that of the state-of-the-art system, the proposed system is 7.1x faster and 9.7x smaller. On the ESC-50, UrbanSound8K, and RAVDESS datasets, we obtain state-of-the-art results with accuracies of 0.961, 0.908, and 0.748, respectively. Our system for the ESC-50 dataset is 1.7x faster and 2.3x smaller than the previous best system. For the RAVDESS dataset, our system is 3.3x smaller than the previous best system. We name our systems "Efficient Residual Audio Neural Networks".