MusicNet-16k + EM for YourMT3

<strong>&lt; UNDER CONSTRUCTION &gt;</strong> <strong>About this version:</strong> This particular variant of the MusicNet dataset has been resampled to a 16 kHz-mono-16-bit-wav format, which makes it more suitable for certain audio processing tasks, particularly those that require lower sampling rates. We redistribute this data as a part of YourMT3 project. The license for redistribution is attached. Moreover, this version of the dataset includes various split options derived from previous works on automatic music transcription as python dictionary (see README.md). Below is a brief description of available split options: <pre><code>MUSICNET_SPLIT_INFO = { 'train_mt3': [], # the first 300 songs are synth dataset, while the remaining 300 songs are acoustic dataset. 'train_mt3_synth' : [], # Note: this is not the synthetic dataset of EM (MIDI Pop 80K) nor pitch-augmented. Just recording of MusicNet MIDI, split by MT3 author's split. But not sure if they used this (maybe not). 'train_mt3_acoustic': [], 'validation_mt3': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'validation_mt3_synth': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'validation_mt3_acoustic': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'test_mt3_acoustic': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], 'train_thickstun': [], # the first 327 songs are synth dataset, while the remaining 327 songs are acoustic dataset. 'test_thickstun': [1819, 2303, 2382], 'train_mt3_em': [], # 293 tracks. MT3 train set - 7 missing tracks[2194, 2211, 2227, 2230, 2292, 2305, 2310], ours 'validation_mt3_em': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], # ours 'test_mt3_em': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], # ours 'train_em_table2' : [], # 317 tracks. Whole set - 7 missing tracks[2194, 2211, 2227, 2230, 2292, 2305, 2310] - 6 test_em 'test_em_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # strings and winds from Cheuk's split, using EM annotations 'test_cheuk_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # strings and winds from Cheuk's split, using Thickstun's annotations }</code></pre> <strong>About MusicNet:</strong> The MusicNet dataset, originally released in 2016 by Thickstun et al., "Learning Features of Music from Scratch". It is a collection of music recordings annotated with labels for various tasks, such as automatic music transcription, instrument recognition, and genre classification. The original dataset contains over 330 hours of audio, sourced from various public domain recordings of classical music, and is labeled with instrument activations and note-wise annotations. <strong>About MusicNet EM:</strong> MusicNetEM are refined labels for the MusicNet dataset, in the form of MIDI files. They are aligned with the recordings, with onset timing within 32ms. They were created using an EM process, similar to the one described in the Ben Maman and Amit H. Bermano, "Unaligned Supervision for Automatic Music Transcription in The Wild". Their split (Table 2 of this paper) derived from another paper, Kin Wai Cheuk et al., "ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data". <strong>License:</strong> CC-BY-4.0< 建设中 > <strong>关于本版本：</strong> 本MusicNet数据集的该变体已被重采样为16 kHz-单声道-16位WAV格式，使其更适用于部分音频处理任务，尤其是对采样率要求较低的任务。我们将本数据作为YourMT3项目的一部分进行重新分发，重新分发许可已随附文件提供。此外，本版本的数据集包含了源自既往自动音乐转录研究的多种划分方案，以Python字典形式存储（详见README.md）。以下为可用划分方案的简要说明： <pre><code>MUSICNET_SPLIT_INFO = { 'train_mt3': [], # 前300首为合成数据集，剩余300首为原声数据集。 'train_mt3_synth' : [], # 注意：本数据集并非EM（MIDI Pop 80K）的合成数据集，也未经过音高增强处理，仅为MusicNet MIDI的录音，采用MT3作者的划分方案，但不确定其是否实际使用了该划分（亦有可能未使用）。 'train_mt3_acoustic': [], 'validation_mt3': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'validation_mt3_synth': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'validation_mt3_acoustic': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], 'test_mt3_acoustic': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], 'train_thickstun': [], # 前327首为合成数据集，剩余327首为原声数据集。 'test_thickstun': [1819, 2303, 2382], 'train_mt3_em': [], # 共293条音轨。MT3训练集缺失7条音轨[2194, 2211, 2227, 2230, 2292, 2305, 2310]，本版本补充完整。 'validation_mt3_em': [1733, 1765, 1790, 1818, 2160, 2198, 2289, 2300, 2308, 2315, 2336, 2466, 2477, 2504, 2611], # 本版本补充的验证集 'test_mt3_em': [1729, 1776, 1813, 1893, 2118, 2186, 2296, 2431, 2432, 2487, 2497, 2501, 2507, 2537, 2621], # 本版本补充的测试集 'train_em_table2' : [], # 共317条音轨。完整数据集缺失7条音轨[2194, 2211, 2227, 2230, 2292, 2305, 2310]，其中6条用作test_em测试集。 'test_em_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # 采用EM标注的Cheuk划分中的弦乐与木管乐子集 'test_cheuk_table2' : [2191, 2628, 2106, 2298, 1819, 2416], # 采用Thickstun标注的Cheuk划分中的弦乐与木管乐子集 }</code></pre> <strong>关于MusicNet：</strong> MusicNet数据集由Thickstun等人于2016年在《Learning Features of Music from Scratch》一文中首次发布。该数据集收录了多首音乐录音，并标注了适用于多种任务的标签，涵盖自动音乐转录、乐器识别与流派分类等方向。原始数据集包含超过330小时的音频素材，均来自公有领域的古典音乐录音，并标注了乐器激活状态与逐音符注释信息。 <strong>关于MusicNet EM：</strong> MusicNetEM是MusicNet数据集的精细化标注版本，以MIDI文件形式呈现，与原录音严格对齐，其音符起始时间误差不超过32毫秒。该标注流程采用了与Ben Maman及Amit H. Bermano在《Unaligned Supervision for Automatic Music Transcription in The Wild》一文中描述的期望最大化（EM）流程相似的方法生成。本版本采用的划分方案（对应论文表2）源自Kin Wai Cheuk等人的论文《ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data》。 <strong>许可协议：</strong> CC-BY-4.0