Listening to the Environment: Applying Deep Learning Techniques for Robust Environmental Sound Classification

Classification of environmental sounds plays an important role in various usages beginning from evaluating the city plans to materializing the ecological monitoring systems. Conventional soundscape separation methods using audio processing frequently typecast them as monotonous and not connected due to overlapping soundscapes from different sources. This research presents a deep model that takes into account the advantages of convolutional neural networks (CNN) and recurrent neural networks (RNN) to make more accurate environment sound classification tasks. The model is capable of such by representing the audio inputs that are being converted to spectrogram forms which convey both the spectral and the temporal features needed for quality sound classification. The validation of the model had to be done, using the metrics which complemented Accuracy, Precision recall, F1-score, and others. These three indices: Sensitivity, Specificity, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) were used for evaluating the model’s capabilities. The results reflect the average sensitivity (over 86 % for all categories) and specificity (over 95% for all categories) with an AUC-ROC value of more than.93 across the different sound classes of environmental sound, showing the model's high discriminative skill. The parameter reveals that the model is capable of successfully identifying the composition of sound signals by categorization amidst other acoustic elements and interference. Contrastively, when we compared to traditional approaches like Support Vector Machines (SVM), the deep learning model outperformed in tissue type recognition and AUC-ROC matrix both in terms of being more sensitive and accurate enough to conclude that advanced neural networks are suitable for such kind of the tasks. Not only does the study add to the knowledge about acoustic phenomena, but also it is the next step toward the creation of a more dependable on-the-spot monitoring apparatus for environmental and urban planning.