Audio Classification Using Ensemble Model

Audio classification is essential for numerous ap-plications, including environmental sound monitoring, speech recognition systems and music genre classification. The ability to accurately classify audio data improves the functionality of these systems, leading to improved user experiences and more effective automation. Accurate audio classification can improve emergency response systems by detecting distress sounds, en-hance smart home devices with better sound recognition and advance healthcare through precise classification of respiratory sounds. Machine learning has transformed audio classification by enabling systems to automatically learn patterns in audio data, enhancing applications like speech recognition and environmental sound monitoring. Convolutional Neural Networks(CNNs), in particular, have proven highly effective for audio classification by extracting hierarchical features from audio spectrograms. This paper proposes an efficient ensemble model involving VGG16, ResNet50 and DenseNet121. The audio data are first converted to spectrograms, which are then used as inputs for the CNN models. The individual pre-trained models are modified to adapt their classifiers to the specific audio classification task, with the number of output classes determined by the dataset. The ensemble model combines the outputs from the three networks, incorporating batch normalization and dropout to improve generalization. By leveraging the complementary strengths of VGG16, ResNet50 and DenseNet121, the ensemble model shows an impressive accuracy of 97.4% on the UrbanSound8K dataset.