图 书 馆
用于音频场景分析的稳定自回归模型的学习字典Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis |
|
| 课程网址: | http://videolectures.net/icml09_cho_ldsamasa/ |
| 主讲教师: | Youngmin Cho |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we explore an application of basis pursuit to audio scene analysis. The goal of our work is to detect when certain sounds are present in a mixed audio signal. We focus on the regime where out of a large number of possible sources, a small but unknown number combine and overlap to yield the observed signal. To infer which sounds are present, we decompose the observed signal as a linear combination of a small number of active sources. We cast the inference as a regularized form of linear regression whose sparse solutions yield decompositions with few active sources. We characterize the acoustic variability of individual sources by autoregressive models of their time domain waveforms. When we do not have prior knowledge of the individual sources, the coefficients of these autoregressive models must be learned from audio examples. We analyze the dynamical stability of these models and show how to estimate stable models by substituting a simple convex optimization for a difficult eigenvalue problem. We demonstrate our approach by learning dictionaries of musical notes and using these dictionaries to analyze polyphonic recordings of piano, cello, and violin. |
| 关 键 词: | 追求音频场景分析; 线性组合; 自回归模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-07：lxf |
| 阅读次数: | 46 |