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Explore the intersection of neural audio compression and language modeling in this comprehensive summer school lecture that delves into cutting-edge techniques for processing and understanding audio data through deep learning approaches. Learn how neural networks can be applied to compress audio signals while preserving essential information, and discover the connections between audio compression methods and language modeling frameworks. Examine state-of-the-art architectures and algorithms used in neural audio processing, including autoencoder-based compression schemes, perceptual loss functions, and rate-distortion optimization techniques. Understand how language modeling principles can be adapted and applied to audio data, exploring sequence-to-sequence models, attention mechanisms, and transformer architectures in the audio domain. Investigate practical applications of these technologies in speech processing, music analysis, and audio generation tasks. Gain insights into the challenges and opportunities in combining compression efficiency with semantic understanding of audio content, and explore recent research developments that bridge the gap between traditional signal processing and modern machine learning approaches to audio analysis.
