Athens Urban Soundscape (ATHUS): A Dataset for Urban Soundscape Quality Recognition.

Soundscape can be regarded as the auditory landscape, conceived individually or at collaborative level. This paper presents ATHUS (ATHens Urban Soundscape), a dataset of audio recordings of ambient urban sounds, which has been annotated in terms of the corresponding perceived soundscape quality. To build our dataset, several users have recorded sounds using a simple smartphone application, which they also used to annotate the recordings, in terms of the perceived quality of the soundscape (i.e. level of “pleasantness”), in a range of 1 (unbearable) to 5 (optimal). The dataset has been made publicly available (in http://users.iit.demokritos.gr/~tyianak/soundscape) as an audio feature representation form, so that it can directly be used in a supervised machine learning pipeline without need for feature extraction. In addition, this paper presents and publicly provides (https://github.com/tyiannak/soundscape_quality) a baseline approach, which demonstrates how the dataset can be used to train a supervised model to predict soundscape quality levels. Experiments under various setups using this library have demonstrated that Support Vector Machine Regression outperforms SVM Classification for the particular task, which is something expected if we consider the gradual nature of the soundscape quality labels. The goal of this paper is to provide to machine learning engineers, working on audio analytics, a first step towards the automatic recognition of soundscape quality in urban spaces, which could lead to powerful assessment tools in the hands of policy makers with regards to noise pollution and sustainable urban living.