Bayesian nonparametric spectrogram modeling based on infinite factorial infinite hidden Markov model
Bayesian nonparametric spectrogram modeling based on infinite factorial infinite hidden Markov model
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DOI：
10.1109/aspaa.2011.6082324
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
M. Nakano;Jonathan Le Roux;H. Kameoka;Tomohiko Nakamura;Nobutaka Ono;S. Sagayama
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文献类型：
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作者：
M. Nakano;Jonathan Le Roux;H. Kameoka;Tomohiko Nakamura;Nobutaka Ono;S. Sagayama
本文提出了一种用于音乐信号分析的贝叶斯非参数潜在源发现方法。在音频信号分析中，一个重要的目标是通过音乐转录、源分离或音符级别操作等应用将音乐信号分解为单独的音符。最近，潜在变量分解的使用，特别是非负矩阵分解（NMF），已经成为一个非常活跃的研究领域。这些方法面临两个相互依赖的问题：首先，乐器声音经常表现出时变频谱，掌握这种时变性质是表征每种乐器多样性的重要因素；此外，在许多情况下，我们事先并不知道源的数量以及演奏的乐器。传统的分解通常无法解决这些问题，因为它们面临自动确定源数量和自动将光谱分组为单个事件的困难。我们通过开发 NMF 和隐马尔可夫模型 (HMM) 的贝叶斯非参数融合来解决这两个问题。我们的模型将音乐频谱图分解为自动估计数量的组件，每个组件都包含一个 HMM，其状态数量也是根据数据自动估计的。
This paper presents a Bayesian nonparametric latent source discovery method for music signal analysis. In audio signal analysis, an important goal is to decompose music signals into individual notes, with applications such as music transcription, source separation or note-level manipulation. Recently, the use of latent variable decompositions, especially nonnegative matrix factorization (NMF), has been a very active area of research. These methods are facing two, mutually dependent, problems: first, instrument sounds often exhibit time-varying spectra, and grasping this time-varying nature is an important factor to characterize the diversity of each instrument; moreover, in many cases we do not know in advance the number of sources and which instruments are played. Conventional decompositions generally fail to cope with these issues as they suffer from the difficulties of automatically determining the number of sources and automatically grouping spectra into single events. We address both these problems by developing a Bayesian nonparametric fusion of NMF and hidden Markov model (HMM). Our model decomposes music spectrograms in an automatically estimated number of components, each of which consisting in an HMM whose number of states is also automatically estimated from the data.