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Recent efforts have been made on acoustic scene classification in the audiosignal processing community. In contrast, few studies have been conducted onacoustic scene clustering, which is a newly emerging problem. Acoustic sceneclustering aims at merging the audio recordings of the same class of acousticscene into a single cluster without using prior information and trainingclassifiers. In this study, we propose a method for acoustic scene clusteringthat jointly optimizes the procedures of feature learning and clusteringiteration. In the proposed method, the learned feature is a deep embedding thatis extracted from a deep convolutional neural network (CNN), while theclustering algorithm is the agglomerative hierarchical clustering (AHC). Weformulate a unified loss function for integrating and optimizing these twoprocedures. Various features and methods are compared. The experimental resultsdemonstrate that the proposed method outperforms other unsupervised methods interms of the normalized mutual information and the clustering accuracy. Inaddition, the deep embedding outperforms many state-of-the-art features.

Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration