Abstract:
Urban environments are awash with myriad
sounds, among which impulsive noises stand distinct due to their
brief and often disruptive nature. As cities evolve and expand,
the accurate classification and management of these impulsive
sounds become paramount for urban planners, environmental
scientists, and public health advocates. This paper introduces a
novel framework leveraging the Bidirectional Long Short-Term
Memory (BiLSTM) Network for the systematic categorization of
impulsive urban sounds. Traditional methodologies often falter
in recognizing the nuanced intricacies of such noises. In contrast,
the presented BiLSTM-based approach adapts to the temporal
variability intrinsic to these sounds, thereby enhancing
classification accuracy. The research harnesses an expansive
dataset, curated from various urban settings, to train and
validate the model. Preliminary findings suggest that our
BiLSTM framework outperforms existing models, with a marked
increase in both specificity and sensitivity metrics. The outcome
of this study holds profound implications for city acoustics
management, noise pollution control, and urban health
interventions. Moreover, the framework's adaptability paves the
way for its application across diverse acoustic landscapes beyond
the urban realm. Future endeavors should seek to further
optimize the model by integrating more diverse soundscapes and
addressing potential biases in data collection.
