Automatic Music Detection in Television Productions

This paper presents methods for the automatic detection of music within audio streams, in the foreor background. The problem occurs in the context of a real-world application, namely, the analysis of TV productions w.r.t. the use of music. In contrast to plain speech/music discrimination, the problem of detecting music in TV productions is extremely difficult, since music is often used to accentuate scenes while concurrently speech and any kind of noise signals might be present. We present results of extensive experiments with a set of standard machine learning algorithms and standard features, investigate the difference between frame-level and clip-level features, and demonstrate the importance of the application of smoothing functions as a post-processing step. Finally, we propose a new feature, called Continuous Frequency Activation (CFA), especially designed for music detection, and show experimentally that this feature is more precise than the other approaches in identifying segments with music in audio streams.