# An Unsupervised Deep Learning System for Acoustic Scene Analysis
## 1 Introduction  
Acoustic scene analysis, has received a lot of research attention recently [1; 2], which aims to recognize acoustic environments [3; 4]. It finds applications in many audio devices, such as cars, robots, context-aware mobile devices, and intelligent monitoring systems. Conventional acoustic scene analysis was mainly supervised, and was named acoustic scene classification. A challenge named detection and classification of acoustic scenes and events, which focuses on the acoustic scene classification, has been launched for several years as well. The supervised methods classify audio segments or frame-level features into predefined acoustic environments using a classifier, such as the support vector machine [5], Gaussian mixture model [6], or deep convolutional neural network [7; 8]. With the rapid development of multimedia technologies, a large number of unlabeled, real-world audio data points are being collected everyday. Analyzing the unlabeled data effectively is an important and challenging problem. However, the effectiveness of supervised acoustic scene classification relies heavily on the quality of manually-labeled data. It is known that labeling large-scale unlabeled acoustic data manually for the classifier training is time-consuming and expensive. Moreover, manuallabels cannot be always accurate, which brings new challenges into the model training procedure [9]. In addition, the semantic labeling of acoustic scenes is also challenging, since the predefined categories may contain a hierarchical structure, and a real-world acoustic scene may contain multiple labels or unclearly-defined scenarios [1]. Unsupervised learning provides a solution to the aforementioned problems, as it does not require predefined label set and manually-labeled training data. In unsupervised learning, clustering is the method to group a set of data so that data in the same cluster are more similar than to those in other groups. It is usually used to analyze the statistic characteristics of the data. In recent years, few clustering methods have been developed [10; 11]. However, unsupervised acoustic scene analysis has not been well studied yet. Traditional methods partition audio clips into a set of acoustic scenes by a clustering algorithm, such as spectral clustering [12], co-clustering [13], or hierarchical clustering [14]. Because the acoustic features of the audio are usually high dimensional, it is difficult to apply a clustering algorithm directly to large-scale audio data. To deal with this problem, it is necessary to first reduce the dimensions of the features by a dimensionality reduction method. For example, [PERSON] et al. first used sparse subspace clustering (SSC) with a random sketching method to reduce the dimensions of some features for a low computational cost, and then adopted an online low-rank subspace clustering (OLRSC)-based algorithm for the acoustic scene clustering [15]. They further improved the performance using a joint clustering algorithm, named joint OLRSC (JOLRSC) [9], which takes both the original feature and the feature in the low-rank subspace as the input of the clustering. In this paper, we propose an unsupervised acoustic scene analysis algorithm based on auto-encoder networks, named _joint auto-encoder network with spectral clustering_ (JAESC), for stereo audio clips. Specifically, JAESC first extracts a high-dimensional binaural representation containing spatial information from the stereo audio clips. Then, the auto-encoder network is used to extract a low dimensional bottleneck feature from a high-dimensional acoustic feature and large-scale data via a so-called _bottleneck_ architecture. Finally, we conduct joint clustering for the final partition.