Hierarchical Classification of Environmental Noise Sources Considering the Acoustic Signature of Vehicle Pass-Bys

Abstract

This work is focused on the automatic recognition of environmental noise sources that affect humans' health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens' daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding).To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

“…Valero, in [ 41 ], presents an automatic approach for the classification of road vehicles based on their pass-by signature. The team recorded a dataset with six categories (light vehicles, heavy vehicles, motorcycles, aircrafts, trains and industrial noise), resulting in 90 real-life samples for each category, with a duration of 4 seconds each.…”

BCNDataset: Description and Analysis of an Annotated Night Urban Leisure Sound Dataset

“…Valero [22] presents an automatic approach for the classification of road vehicles by means of their pass-by signatures. The team recorded a dataset with six categories (light vehicles, heavy vehicles, motorcycles, aircrafts, trains, and industrial noise), resulting in 90 sound samples for each category, with a duration of 4 s each.…”

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

“…Although, providing the reader with a comprehensive view of specific machine hearing problems exceeds the goals of this work, the interested reader will find diverse examples of the machine hearing applications throughout the paper. Examples include speaker identification (like in Yuo et al [27]), music genre classification (Tzanetakis and Cook [28]), environmental sound recognition (e.g., the works by Ando [29] and Valero and Alías [30]), audio indexing and retrieval (Richard et al [31]), or CASA (as in the works by Peltonen et al [18], Chu et al [14], Valero and Alías [15]). …”