论文标题
层次线性动力学系统，用于表示记录音频的注释
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio
论文作者
论文摘要
我们寻求在存在异常值的情况下同时对录音中的音符进行分割和分类。用于建模时间序列的选定体系结构是分层线性动力学系统（HLDS）。我们为其参数设置提出了一种新颖的方法。 HLD可以通过两种方式使用HLD：1）同时进行分割和聚类来探索数据，即查找未知的注释，2）在存在异常值的情况下，同时进行音频记录的分段和分类，以查找感兴趣的注释。我们将HLD用于第二目的，因为这是一个更容易的任务，仍然是一个具有挑战性的问题，例如在生物声学领域。每个测试剪辑具有与培训剪辑相同的音符（但不同的实例），还包含外表音符。在测试中，自动决定票据属于哪个兴趣类别。这项工作的两种应用是在有生物源的领域，用于在音轨录制和音乐学中检测动物声音。已经进行了实验，以分割和分类录制音频的音符和音符。
We seek to develop simultaneous segmentation and classification of notes from audio recordings in presence of outliers. The selected architecture for modeling time series is hierarchical linear dynamical system (HLDS). We propose a novel method for its parameter setting. HLDS can potentially be employed in two ways: 1) simultaneous segmentation and clustering for exploring data, i.e. finding unknown notes, 2) simultaneous segmentation and classification of audio recording for finding the notes of interest in the presence of outliers. We adapted HLDS for the second purpose since it is an easier task and still a challenging problem, e.g. in the field of bioacoustics. Each test clip has the same notes (but different instances) as of the training clip and also contain outlier notes. At test, it is automatically decided to which class of interest a note belongs to if any. Two applications of this work are to the fields of bioacoustics for detection of animal sounds in audio field recordings and also to musicology. Experiments have been conducted for segmentation and classification of both avian and musical notes from recorded audio.
