Audio Transformer

Audio transformers are neural network architectures applying the transformer model, known for its success in natural language processing, to audio data analysis. Current research focuses on improving efficiency and performance in tasks like sound event detection, speech synthesis, and music generation, often employing techniques like masked-reconstruction pre-training and adapter tuning to optimize model size and training time. These advancements are leading to more accurate and efficient audio processing in various applications, including bioacoustic monitoring, assistive technologies, and content-based music generation. The development of robust, efficient, and interpretable audio transformers is significantly impacting the field, enabling progress in diverse areas of audio analysis and synthesis.