This paper presents a comprehensive investigation on the detection of applause sounds in audio signals. It focuses on the processing of single microphone recordings in real-time with low latency. A particular concern is the intensity of the applause within the sound mixture and the influence of the interfering sounds on the recognition performance which is investigated experimentally. Well-known feature sets, feature processing and classification methods are compared. Additional low-pass filtering of the feature time series leads to the concept of sigma features and yields further improvements of the detection result.
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