Abstract
The recently-established Laboratory for Recognition and Organization of Speech and Audio (LabROSA) at Columbia has the mission of developing techniques to extract useful information from sound. This covers a range of areas: General-purpose structure discovery and recovery, i.e. the basic segmentation problem over scales from subwords to episodes, and on both time and frequency dimensions; Source/object-based organization: Explaining the observed signal as the mixture of the independent sources that would be percieved by listeners; Special-purpose recognition and characterization for specific domains such as speech (transcription, speaker tracking etc.), music (classification and indexing), and other distinct categories. I will present more details on some current and new projects, including: Tandem acoustic modeling: Noise-robust speech recognition features calculated by a neural net. The Meeting Recorder project: Acoustic information extraction applications in a conventional meeting scenario. Machine Listening: Hearing for autonomous devices in the real world.