Respiratory Rate Dataset - BreathMY_v2

An open-source audio dataset for respiratory rate (RR) estimation research. About The Dataset The dataset has been generated by selecting 150 recordings from the BreathMY dataset, thereby extending it. This novel dataset comprises three distinct repositories of respiratory signals, specifically developed for the rigorous design and evaluation of RR estimation methodologies, covering both noise free and noise-affected scenarios. The constituent datasets include: DR: dataset of noise-free respiratory signals with controlled RR variability, generated through data augmentation techniques. DSN: dataset with additive white Gaussian noise (AWGN) at different signal-to-noise ratio (SNR) levels (0, -6 and -20 dB). DN: dataset with environmental acoustic noise at different SNR (0, -6 and -20 dB). File Format Each audio file is encoded in .wav format and follows the naming convention: DR_9RR_2023_03_15_4_A.wav Where: DR → Identifies the source dataset (e.g., DR). 9RR → Ground truth RR of the signal, expressed in breaths per minute (bpm). Here, RR = 9 bpm. 2023_03_15 → Date of creation or processing of the signal (YYYY_MM_DD). 4 → Patient index recorded on that specific date. Here, it corresponds to the fourth patient recorded on 2023-03-15. A → Alphabetical character used to differentiate files and prevent overwriting when applying data augmentation DR: Dataset with Noise-Free Respiratory Signals The DR dataset consists of 2,550 respiratory signals, each 60 seconds in duration. It was built using two data augmentation techniques applied to the pre-existing dataset: Time Stretching (TS): transforms signals with fast RR into signals with slower RR. It preserves the original waveform but increases the duration of each respiratory cycle. Cycle Replication (CR): transforms signals with slow RR into signals with faster RR. It compresses the duration of each respiratory phase (inhalation and exhalation) and replicates the cycles to complete the recording interval. DSN: Dataset with Additive White Gaussian Noise To evaluate the robustness of the methods under controlled noise conditions, the DSN dataset is generated from DR by adding AWGN to each signal in the DR dataset at three different SNR levels: DSN0 → SNR = 0 dB DSN-6 → SNR = -6 dB DSN-20 → SNR = -20 dB AWGN uniformly affects all frequency components and represents a standard yet demanding scenario for assessing the performance degradation of algorithms. Each subset preserves the same number of signals as DR, resulting in a total of 7,650 signals — that is, 2,550 respiratory signals mixed with AWGN noise for each SNR level. DN: Dataset with Environmental Acoustic Noise The DN dataset simulates real-world environments by adding environmental acoustic noise to each signal from DR at three SNR levels, containing a total of 7,650 signals — that is, 2,550 respiratory signals mixed with environmental acoustic noise for each SNR level: DN0 → SNR = 0 dB DN-6 → SNR = -6 dB DN-20 → SNR = -20 dB The noises used originate from two types of environments: Clinical (Soundsnap): medical equipment, conversations, footsteps in corridors and other typical hospital sounds. Urban (DCASE): ten different acoustic scenes, including streets, public transport, indoor spaces, etc. For each mixture: The type of noise is assigned randomly. A random 60-second segment of noise is extracted to ensure variability. A balanced representation of all noise types is maintained. Noise segments used for training and testing are mutually exclusive, ensuring that no segment appears in both sets and thereby reducing the risk of overfitting. License Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Copyright (c) 2026 QHPC & SP Research Lab This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. You are free to: - Share — copy and redistribute the material in any medium or format - Adapt — remix, transform, and build upon the material Under the following terms: - Attribution — You must give appropriate credit. - NonCommercial — You may not use the material for commercial purposes. Full license text: https://creativecommons.org/licenses/by-nc/4.0/ Contact Alejandro Antonio Salvador Navarro ([e-mail])University of Jaén Francisco Jesús Cañadas Quesada ([e-mail])University of Jaén Juan de la Torre Cruz ([e-mail])University of Jaén Citing When using this dataset, please cite the following publication: Salvador-Navarro, A., De La Torre-Cruz, J., Muñoz-Montoro, A. J., Ranilla-Cortina, S., Carabias-Orti, J. J., Cruz-Molina, J. M., & Cañadas-Quesada, F. J. (2026). Respiratory rate estimation from breath sounds based on deep learning*. Biomedical Signal Processing and Control, 119, 109905. Funding This work was supported in part by the PID2023-146520OB-{C21,C22} funded by MICIU/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by “ERDF/EU”, by the “European Union” or by the “European Union NextGenerationEU/PRTR” and in part by “REPERTORIUM” Project under Grant Agreement 101095065. Horizon Europe. Cluster II. Culture, Creativity and Inclusive society. Call HORIZON-CL2-2022-HERITAGE-01-02.一款用于呼吸频率（respiratory rate，RR）估计研究的开源音频数据集。 关于本数据集 本数据集通过从BreathMY数据集中遴选150条录音并进行扩充构建而成。这款全新数据集包含三类独立的呼吸信号库，专为呼吸频率估计方法的严谨设计与性能评估打造，覆盖无噪声与含噪声两种实验场景。 数据集组成 DR：通过数据增强技术生成的可控呼吸频率变异性无噪声呼吸信号数据集 DSN：添加了不同信噪比（signal-to-noise ratio，SNR）水平（0、-6及-20 dB）的加性高斯白噪声（additive white Gaussian noise，AWGN）数据集 DN：添加了不同信噪比（SNR）水平（0、-6及-20 dB）的环境声学噪声数据集 文件格式 所有音频文件均采用.wav格式编码，命名规则如下： DR_9RR_2023_03_15_4_A.wav 各字段含义如下： DR：标识源数据集（如DR） 9RR：信号的真实呼吸频率，单位为 breaths per minute（bpm，即每分钟呼吸次数），此处RR=9次/分钟 2023_03_15：信号创建或处理的日期，格式为YYYY_MM_DD 4：当日录制的患者编号，此处代表2023年3月15日录制的第4位患者 A：用于区分文件的字母字符，避免在执行数据增强时出现文件覆盖 DR：无噪声呼吸信号数据集 DR数据集包含2550条时长为60秒的呼吸信号，通过对已有数据集应用两种数据增强技术构建而成： 时间拉伸（Time Stretching，TS）：将呼吸频率较快的信号转换为呼吸频率较慢的信号，保留原始波形但延长每个呼吸周期的时长 周期复制（Cycle Replication，CR）：将呼吸频率较慢的信号转换为呼吸频率较快的信号，压缩每个呼吸阶段（吸气与呼气）的时长并复制周期以填满整个录制时长 DSN：加性高斯白噪声数据集 为评估算法在受控噪声环境下的鲁棒性，DSN数据集通过在DR数据集的每条信号中添加三种不同信噪比水平的加性高斯白噪声（AWGN）生成： DSN0：信噪比（SNR）为0 dB DSN-6：信噪比（SNR）为-6 dB DSN-20：信噪比（SNR）为-20 dB 加性高斯白噪声（AWGN）会均匀影响所有频率分量，是评估算法性能衰减的标准且严苛的实验场景。每个子数据集与DR的信号数量一致，因此总共有7650条信号——即每种信噪比水平下均有2550条混合了AWGN的呼吸信号。 DN：环境声学噪声数据集 DN数据集通过在DR数据集的每条信号中添加三种不同信噪比水平的环境声学噪声，以模拟真实应用环境，总共有7650条信号——即每种信噪比水平下均有2550条混合了环境声学噪声的呼吸信号： DN0：信噪比（SNR）为0 dB DN-6：信噪比（SNR）为-6 dB DN-20：信噪比（SNR）为-20 dB 所用噪声源自两类环境： 临床环境（Soundsnap）：医疗设备声响、交谈声、走廊脚步声及其他典型医院环境声响 城市环境（DCASE）：十种不同的声学场景，包括街道、公共交通、室内空间等 针对每一条混合信号： 1. 噪声类型随机分配 2. 随机截取60秒的噪声片段以保证多样性 3. 确保所有噪声类型的占比均衡 4. 用于训练与测试的噪声片段互不重叠，确保同一噪声片段不会同时出现在训练集与测试集中，从而降低过拟合风险 授权协议 知识共享署名-非商业性使用4.0国际许可协议（Creative Commons Attribution-NonCommercial 4.0 International，CC BY-NC 4.0） 版权所有 © 2026 QHPC与SP研究实验室 本数据集采用知识共享署名-非商业性使用4.0国际许可协议进行授权。 您可自由： - 复制与分发：以任何媒介或格式复制并重新发布本素材 - 改编：对本素材进行再创作、转换并基于其进行二次开发 需遵循以下条款： - 署名：您必须给予恰当的署名认可 - 非商业性使用：您不得将本素材用于商业用途 完整许可协议文本：https://creativecommons.org/licenses/by-nc/4.0/ 联系方式 亚历杭德罗·安东尼奥·萨尔瓦多·纳瓦罗（[e-mail]），哈恩大学 弗朗西斯科·赫苏斯·卡尼亚达斯·克萨达（[e-mail]），哈恩大学 胡安·德拉·托雷·克鲁兹（[e-mail]），哈恩大学 引用说明 使用本数据集时，请引用以下文献： Salvador-Navarro, A., De La Torre-Cruz, J., Muñoz-Montoro, A. J., Ranilla-Cortina, S., Carabias-Orti, J. J., Cruz-Molina, J. M., & Cañadas-Quesada, F. J. (2026). 基于深度学习的呼吸音呼吸频率估计*. 《生物医学信号处理与控制》, 119, 109905. 资助信息 本工作部分受PID2023-146520OB-{C21,C22}项目资助，该项目由MICIU/AEI/10.13039/501100011033资助，并视情况由“欧洲区域发展基金（ERDF，“建设欧洲之路”）”、“欧盟（EU）”、“欧洲联盟”或“欧洲联盟下一代欧盟/PRTR”提供支持；同时部分受“REPERTORIUM”项目（资助协议编号101095065，地平线欧洲计划，第二集群：文化、创意与包容社会，征集编号HORIZON-CL2-2022-HERITAGE-01-02）资助。