Single Channel Audio

Single-channel audio processing focuses on extracting meaningful information and performing complex tasks using only a single audio stream, a common and cost-effective scenario. Current research emphasizes developing robust algorithms, often employing convolutional neural networks, transformers, and recurrent architectures, to address challenges like speaker separation in overlapping speech, accurate distance estimation, and reliable speech transcription even in noisy or reverberant environments. These advancements have significant implications for various applications, including improved voice assistants, enhanced audio forensics, and more realistic audio synthesis for virtual and augmented reality experiences.