Multitask Learning for Frame-level Instrument Recognition
Multitask Learning for Frame-level Instrument Recognition
复制标题
帧级仪器识别的多任务学习
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yi
中科院分区：
文献类型：
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作者：
Yun;Yian Chen;Yi
对于许多音乐分析问题，我们需要知道多乐器音乐作品中每个时间范围内乐器的存在情况。然而，这种帧级仪器识别任务仍然很困难，主要是由于缺乏标记数据集。为了解决这个问题，我们在本文中提出了一个大规模数据集，其中包含具有帧级音高和乐器标签的合成复调音乐。此外，我们提出了一种简单而新颖的网络架构来联合预测每一帧的音高和乐器。通过这种多任务学习方法，可以利用音高信息来预测乐器，反之亦然。而且，通过使用所谓的音乐钢琴卷表示作为模型的主要目标输出，我们的模型还可以预测演奏每个单独音符事件的乐器。我们通过与单任务消融版本和三种最先进的方法进行比较，验证了所提出的帧级仪器识别方法的有效性。我们还展示了所提出的真实世界音乐多音高流媒体方法的结果。为了可重复性，我们将在以下位置共享用于爬取数据并实现建议模型的代码：https://github.com/biboamy/instrument-streaming。
For many music analysis problems, we need to know the presence of instruments for each time frame in a multi-instrument musical piece. However, such a frame-level instrument recognition task remains difficult, mainly due to the lack of labeled datasets. To address this issue, we present in this paper a large-scale dataset that contains synthetic polyphonic music with frame-level pitch and instrument labels. Moreover, we propose a simple yet novel network architecture to jointly predict the pitch and instrument for each frame. With this multitask learning method, the pitch information can be leveraged to predict the instruments, and also the other way around. And, by using the so-called pianoroll representation of music as the main target output of the model, our model also predicts the instruments that play each individual note event. We validate the effectiveness of the proposed method for frame-level instrument recognition by comparing it with its single-task ablated versions and three state-of-the-art methods. We also demonstrate the result of the proposed method for multi-pitch streaming with real-world music. For reproducibility, we will share the code to crawl the data and to implement the proposed model at: https://github.com/biboamy/ instrument-streaming.
DOI：
10.1109/icassp.2014.6854599
发表时间：
2014
期刊：
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影响因子：
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作者：
Giannoulis D
通讯作者：
Giannoulis D
MedleyDB：用于注释密集型 MIR 研究的多轨数据集
DOI：
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发表时间：
2014
期刊：
15th International Society for Music Information Retrieval Conference
影响因子：
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作者：
Bittner, R.
通讯作者：
Bittner, R.