Detecting applause in both audio recordings and real-time performances is relevant in such applications as music information retrieval and spatial audio coding. A combination of mel-frequency cepstral coefficients and low-level descriptors yielded the best classification performance in the experiments. Low-pass filtering of the feature time series leads to the concept of sigma features. Binary misclassification occurs more often when applause and nonapplause with similar amplitudes are simultaneously present.
https://www.aes.org/e-lib/browse.cfm?elib=15926
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is free for AES members and E-Library subscribers.
Learn more about the AES E-Library
Start a discussion about this paper!
