Download A Simple and Effective Spectral Feature for Speech Detection in Mixed Audio Signals We present a simple and intuitive spectral feature for detecting the presence of spoken speech in mixed (speech, music, arbitrary sounds and noises) audio signals. The feature is based on some simple observations about the appearance, in signals that contain speech, of harmonics with characteristic trajectories. Experiments with some 70 hours of radio broadcasts in five different languages demonstrate that the feature is very effective in detecting and delineating segments that contain speech, and that it also seems to be quite general and robust w.r.t. different languages.
Download Quad-Based Audio Fingerprinting Robust to Time and Frequency Scaling We propose a new audio fingerprinting method that adapts findings from the field of blind astrometry to define simple, efficiently representable characteristic feature combinations called quads. Based on these, an audio identification algorithm is described that is robust to large amounts of noise and speed, tempo and pitch-shifting distortions. In addition to reliably identifying audio queries that are modified in this way, it also accurately estimates the scaling factors of the applied time/frequency distortions. We experimentally evaluate the performance of the method for a diverse set of noise, speed and tempo modifications, and identify a number of advantages of the new method over a recently published distortioninvariant audio copy detection algorithm.