Sonicscape: unveiling and alleviating natural sound proportions in urban environmental noise pollution

We propose SonicScape, a novel CRNN-based model specifically designed to effectively tackle the issue of excessive natural sounds in urban environments. This is achieved by leveraging mel spectrograms. Our model aims to evaluate and mitigate the impact of natural sound proportions on urban environmental noise pollution, with a particular focus on the city of Shenzhen. To ensure accurate assessment, we introduce a novel evaluation metric called NSP (Natural Sound Proportions), which quantifies the contribution of natural sounds to the overall noise levels. To support our research, we have created the Shenzhen Environmental Sound Dataset, which addresses the lack of publicly available datasets by incorporating unique sound characteristics specific to Shenzhen. SonicScape combines the power of CNNs for efficient feature extraction with GRUs for precise temporal modeling, resulting in superior performance on this dataset. In conclusion, we suggest future work to expand the dataset through an ongoing annotation process, explore more robust annotation and computation methods for NSP, and enhance the models by incorporating cutting-edge techniques from the domains of image recognition and time series analysis. This will improve classification and regression tasks based on spectrograms.