Deep Learning for Audio Processing

Deep Learning for Audio Processing is a specialized area of artificial intelligence that applies deep neural network architectures to analyze, understand, and synthesize audio signals. By leveraging models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this field processes audio data, often represented as raw waveforms or time-frequency representations like spectrograms, to automatically learn complex, hierarchical features. This approach has led to state-of-the-art performance in a wide range of tasks including automatic speech recognition, music information retrieval, sound event detection, and audio synthesis, largely supplanting traditional methods that relied on manually engineered features.