A missing feature approach to instrument identification in polyphonic music
A missing feature approach to instrument identification in polyphonic music
复制标题
复调音乐中乐器识别的缺失特征方法
DOI：
10.1109/aspaa.2003.1285807
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
G.J. Brown
中科院分区：
文献类型：
--
作者：
J. Eggink;G.J. Brown
仅提供摘要形式。高斯混合模型（GMM）分类器已被证明是由一个单一的乐器演奏的单声道音乐的良好的乐器识别性能。然而，许多应用（例如自动音乐转录）需要从复调、多乐器录音中识别乐器。我们解决这个问题，将丢失特征理论的思想纳入GMM分类器。具体地，由来自干扰音调的能量主导的频率区域被标记为不可靠的并且从分类过程中排除。这种方法已被评估随机双音和弦和摘录从市售光盘，有希望的结果。
Summary form only given. Gaussian mixture model (GMM) classifiers have been shown to give good instrument recognition performance for monophonic music played by a single instrument. However, many applications (such as automatic music transcription) require instrument identification from polyphonic, multi-instrumental recordings. We address this problem by incorporating ideas from missing feature theory into a GMM classifier. Specifically, frequency regions that are dominated by energy from an interfering tone are marked as unreliable and excluded from the classification process. This approach has been evaluated on random two-tone chords and an excerpt from a commercially available compact disc, with promising results.