Joint Estimation of Chords and Downbeats From an Audio Signal
Joint Estimation of Chords and Downbeats From an Audio Signal
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DOI：
10.1109/tasl.2010.2045236
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
H. Papadopoulos;Geoffroy Peeters
中科院分区：
文献类型：
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作者：
H. Papadopoulos;Geoffroy Peeters
我们提出了一种从音频文件中联合估计和弦级数和降拍的新方法。音乐信号在和声和节奏方面是高度结构化的。在这篇文章中，我们打算表明，整合和弦和节拍结构之间相互依赖的知识可以增强对这些音乐属性的估计。为此，我们提出了一种特定的隐马尔可夫模型拓扑，该模型能够建模和弦对度量结构的依赖。该模型允许我们考虑节拍结构复杂的乐曲，如节拍的增加、节拍的删除或节拍的变化。该模型是在披头士乐队的一大套流行音乐歌曲上进行评估的，这些歌曲呈现了各种节拍结构。我们比较了半自动模型和全自动模型，在半自动模型中，节拍位置被注释，而在全自动模型中，节拍跟踪器被用作系统的前端。结果表明，可以根据乐曲的和声结构来估计乐曲的低拍位置，而和弦级数估计则得益于节拍和和弦结构之间的相互作用。
We present a new technique for joint estimation of the chord progression and the downbeats from an audio file. Musical signals are highly structured in terms of harmony and rhythm. In this paper, we intend to show that integrating knowledge of mutual dependencies between chords and metric structure allows us to enhance the estimation of these musical attributes. For this, we propose a specific topology of hidden Markov models that enables modelling chord dependence on metric structure. This model allows us to consider pieces with complex metric structures such as beat addition, beat deletion or changes in the meter. The model is evaluated on a large set of popular music songs from the Beatles that present various metric structures. We compare a semi-automatic model in which the beat positions are annotated, with a fully automatic model in which a beat tracker is used as a front-end of the system. The results show that the downbeat positions of a music piece can be estimated in terms of its harmonic structure and that conversely the chord progression estimation benefits from considering the interaction between the metric and the harmonic structures.