AI-Generated Audio Recognition System Based on Deep Learning

The study observes that breakthroughs in the development of AI have made it possible to produce sounds in audio interfaces that mimic human speech to the extent that they are extremely hard to distinguish from natural speech. The objective of this work is to design a detection model using a CRNN as the primary model type of the hybrid system. By integrating CNNs for spectral features and LSTM for temporal features of the audio data, the architecture is planned to emphasize the spectral and temporal properties of the audio which is crucial for the distinction between AI-synthesized and human- created audio. The study employs the Speaker Recognition Audio Dataset available at Kaggle, which provides a wide variety of samples of speaker audio. To increase the robustness in the model, the following steps of data preprocessing have been employed: noise reduction, normalization, and data enhanced by techniques like pitch shift and added noise. Data preprocessing is performed by Feature extraction as key pre-processing techniques such as MFCC and STFT are used in the preprocessing stage to capture certain details that are important for detection. Its ability to determine whether the audio is AI-generated of different circumstances is the key basis of its assessment through the model’s accuracy, F1 score, and recall. Recent investigations indicate that adaptively incorporating adversarial training and the feeding of extra diverse data samples may improve the model’s resilience. Thus, this work emphasizes that deep learning models can be quite useful in identifying whether the audio is fake or real, providing a basic strategy for mitigating the threats involving synthesized audio in computerized communication.