The identity of musical instruments is reflected in the acoustic attributes of musical notes played with them. Recently, it has been argued that these characteristics of musical identity (or timbre) can be best captured through an analysis that encompasses both time and frequency domains; with a focus on the modulations or changes in the signal in the spectrotemporal space. This representation mimics the spectrotemporal receptive field (STRF) analysis believed to underlie processing in the central mammalian auditory system, particularly at the level of primary auditory cortex. How well does this STRF representation capture timbral identity of musical instruments in continuous solo recordings remains unclear. The current work investigates the applicability of the STRF feature space for instrument recognition in solo musical phrases and explores best approaches to leveraging knowledge from isolated musical notes for instrument recognition in solo recordings. The study presents an approach for parsing solo performances into their individual note constituents and adapting back-end classifiers using support vector machines to achieve a generalization of instrument recognition to off-the-shelf, commercially available solo music.

中文翻译：

---

孤立音符和独奏乐句中乐器识别的仿生谱时间特征

乐器的特性体现在用它们演奏的音符的声学属性上。最近，有人认为，音乐身份（或音色）的这些特征可以通过涵盖时域和频域的分析来最好地捕捉；重点关注光谱时间空间中信号的调制或变化。这种表示模仿了光谱时间感受野 (STRF) 分析，该分析被认为是哺乳动物中央听觉系统处理的基础，特别是在初级听觉皮层水平。这种 STRF 表示如何在连续独奏录音中捕捉乐器的音色特性尚不清楚。目前的工作调查了 STRF 特征空间在独奏音乐乐句中乐器识别的适用性，并探索了利用孤立音符知识在独奏录音中进行乐器识别的最佳方法。该研究提出了一种将独奏表演解析为单独的音符成分并使用支持向量机调整后端分类器的方法，以实现乐器识别对现成的商用独奏音乐的泛化。