Bayesian semi-supervised audio event transcription based on Markov indian buffet process
Yasunori Ohishi, Daichi Mochihashi, Tomoko Matsui, Masahiro Nakano, Hirokazu Kameoka, Tomonori Izumitani, Kunio Kashino · 2013
We present a novel generative model for audio event transcription that recognizes “events” on audio signals including multiple kinds of overlapping sounds. In the proposed model, firstly, the overlapping audio events are modeled based on nonnegative matrix factorization into which Bayesian nonparametric approaches: the Markov Indian buffet process and the Chinese restaurant process, are incorporated. This approach allows us to automatically transcribe the events while avoiding the model selection problem by assuming a countably infinite number of possible audio events in the input signal. Then, Bayesian logistic regression annotates the audio frames with the multiple event labels in a semi-supervised learning setup. Experimental results show that our model can better annotate an audio signal in comparison with a baseline method. Additionally, we verify that our infinite generative model is also able to detect unknown audio events that are not included in the training data.