Neural network-based signal processing techniques for near-field acoustics

Near-field acoustics is spatially and temporally broad. This study investigates near-field acoustics neural network sound processing. The major goals are to develop novel signal processing methods and understand sound behavior near its source. Attention processes found complicated patterns, CNNs investigated space, and LSTM networks depicted temporal relationships. The recommended technique was compared to principal component analysis, hidden Markov models, wavelet transform, spectrum analysis, and non-negative matrix factorization. Results reveal that the recommended method is more accurate than current ones. Traditional methods scored 0.70–0.79, whereas the recommended approach scored 0.85. Modern approaches are accurate to 0.88, compared to 0.75–0.82. Finally, neural network-based signal processing in near-field acoustics may reveal sound behavior near its source. The proposed technology uses CNNs, LSTMs, and attention. It accurately captures near-field audio data's complicated temporal and spatial features. The comparison analysis found the recommended approach more accurate than traditional methods. It may help us understand near-field acoustics and its applications in industrial quality, noise reduction, location, and management.