Unified AI for Accurate Audio Anomaly Detection

Existing methodologies often struggle to balance computational efficiency with high performance. Traditional techniques, such as the Fourier transforms and Mel - frequency cepstral coefficients (MFCCs), have been widely used for feature extraction. While these methods are computationally efficient, they lack the flexibility to handle complex, noisy, or highly dynamic datasets. On the other hand, deep learning - based approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), offer superior performance but often require extensive computational resources, making them less viable for real - time or resource - constrained applications. Recent works, including those by Patel et al. [1] and Tan et al. [2], have explored specific solutions to these challenges. Patel et al. introduced a wavelet - based noise reduction method that demonstrated improvements in signal clarity. Similarly, Tan et al. proposed lightweight deep learning models optimized for real - time anomaly detection, addressing latency concerns. While these contributions are noteworthy, they often lack generalizability across diverse datasets and fail to address scalability for large - scale deployments. Our research aims to overcome these limitations by proposing a unified framework that seamlessly integrates advanced preprocessing techniques with flexible machine learning architectures.