Analyzing Song Structure with Spectral Clustering
نویسندگان
چکیده
Many approaches to analyzing the structure of a musical recording involve detecting sequential patterns within a selfsimilarity matrix derived from time-series features. Such patterns ideally capture repeated sequences, which then form the building blocks of large-scale structure. In this work, techniques from spectral graph theory are applied to analyze repeated patterns in musical recordings. The proposed method produces a low-dimensional encoding of repetition structure, and exposes the hierarchical relationships among structural components at differing levels of granularity. Finally, we demonstrate how to apply the proposed method to the task of music segmentation.
منابع مشابه
Nyström Sampling Depends on the Eigenspec- Trum Shape of the Data
Spectral clustering has shown a superior performance in analyzing the cluster structure. However, its computational complexity limits its application in analyzing large-scale data. To address this problem, many low-rank matrix approximating algorithms are proposed, including the Nyström method – an approach with proven approximate error bounds. There are several algorithms that provide recipes ...
متن کاملNew Methods for Spectral Clustering
Analyzing the affinity matrix spectrum is an increasingly popular data clustering method. We propose three new algorithmic components which are appropriate for improving performance of spectral clustering. First, observing the eigenvectors suggests to use a K-lines algorithm instead of the commonly applied K-means. Second, the clustering works best if the affinity matrix has a clear block struc...
متن کاملConsistency of Spectral Clustering
Consistency is a key property of all statistical procedures analyzing randomly sampled data. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of the popular family of spectral clustering algorithms, which clusters the data with the help of eigenvectors of graph Laplacian matrices. We develop new meth...
متن کاملSpectral Clustering and Embedding with Hidden Markov Models
Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering of vector inputs is straightforward, extensions to structured data or time-series data remain less explored. This paper proposes a clustering method for time-series data that couples non-parametric spectral clustering with para...
متن کاملParallel Spectral Clustering Algorithm for Large-Scale Community Data Mining
The spectral clustering algorithm has been shown to be very effective in finding clusters of non-linear boundaries. Unfortunately, spectral clustering suffers from the scalability problem in both memory use and computational time. In this work, we parallelize the algorithm by dividing both memory use and computation on distributed machines. Empirical study on some small datasets shows the accur...
متن کامل