# Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

arXiv: https://arxiv.org/abs/2102.03049 · HTML: https://mlsanity.com/paper/2102.03049/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 92 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Convolutional-Recurrent Neural Networks for Speech Enhancement](https://mlsanity.com/paper/1805.00579/) (2018)
- [Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks](https://mlsanity.com/paper/1907.05708/) (2019)
- [Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases](https://mlsanity.com/paper/2002.03894/) (2020)
- [Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning](https://mlsanity.com/paper/2004.08287/) (2020)
- [Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings](https://mlsanity.com/paper/1604.00861/) (2016)
- [Convolutional neural network for breathing phase detection in lung sounds](https://mlsanity.com/paper/1903.10251/) (2019)
- [Development of a Respiratory Sound Labeling Software for Training a Deep Learning-Based Respiratory Sound Analysis Model](https://mlsanity.com/paper/2101.01352/) (2021)
- [Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information](https://mlsanity.com/paper/2011.13194/) (2020)
- [Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases](https://mlsanity.com/paper/2012.13699/) (2020)

## Built on by (most cited first, 12 of 12)

- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [An Update on a Progressively Expanded Database for Automated Lung Sound Analysis](https://mlsanity.com/paper/2102.04062/) (2021)
- [Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings](https://mlsanity.com/paper/2106.15835/) (2021)
- [Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification](https://mlsanity.com/paper/2501.00064/) (2024)
- [A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training](https://mlsanity.com/paper/2107.04229/) (2021)
- [Multi-path Convolutional Neural Networks Efficiently Improve Feature Extraction in Continuous Adventitious Lung Sound Detection](https://mlsanity.com/paper/2107.04226/) (2021)
- [Exploring traditional machine learning for identification of pathological auscultations](https://mlsanity.com/paper/2209.00672/) (2022)
- [Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory Diseases](https://mlsanity.com/paper/2405.07442/) (2024)
- [EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection](https://mlsanity.com/paper/2509.01153/) (2025)
- [Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis](https://mlsanity.com/paper/2602.15909/) (2026)
- [Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio](https://mlsanity.com/paper/2606.23243/) (2026)

## Abstract (arXiv)

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.