Abstract
Audio signals are a type of high-dimensional data, and their clustering is critical. However, distance calculation failures, inefficient index trees, and cluster overlaps, derived from the equidistance, redundant attribute, and sparsity, respectively, seriously affect the clustering performance. To solve these problems, an audio-signal clustering algorithm based on the sequential Psim matrix and Tabu Search is proposed. First, the audio signal similarity is calculated with the Psim function, which avoids the equidistance. The data is then organized using a sequential Psim matrix, which improves the indexing performance. The initial clusters are then generated with differential truncation and refined using the Tabu Search, which eliminates cluster overlap. Finally, the K-Medoids algorithm is used to refine the cluster. This algorithm is compared to the K-Medoids and spectral clustering algorithms using UCI waveform datasets. The experimental results indicate that the proposed algorithm can obtain better Macro-F1 and Micro-F1 values with fewer iterations.
Highlights
Audio signal clustering forms the basis for speech recognition, audio synthesis, audio retrieval, etc
It can be seen that the iterations for the PM-TS clustering algorithm are lesser than those of the K-Medoids and spectral clustering algorithms, indicating that our proposed method can obtain a more precise initial cluster and converges faster
4.5 Whole-performance analysis The experimental results are averaged and presented in Table 2; they illustrate the better performance of the PM-TS clustering algorithm compared to the K-Medoids and spectral clustering algorithms
Summary
Audio signal clustering forms the basis for speech recognition, audio synthesis, audio retrieval, etc. Audio signals are considered as high-dimensional data, with dimensionalities of more than 20 [1]. The first method reduces the data dimensionality with attribute conversion or reduction and performs clustering. Equidistance, the redundant attribute, and sparsity are the fundamental factors affecting the clustering performance of high-dimensional data [9]. Equidistance renders the distance between any two points in a high-dimensional space approximately equal, leading to a failure in the clustering algorithm, based on the distance. To solve the clustering problems owing to equidistance, the redundant attribute, and sparsity, an efficient audio signal clustering algorithm is proposed, by integration with the Psim matrix and Tabu Search. The initial clusters are iteratively refined with the K-Medoids algorithm, until all the cluster medoids are stable
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