Clustering algorithm for audio signals based on the sequential Psim matrix and Tabu Search

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.