Polyphonic sound event detection using multi label deep neural networks

Cakir, Emre; Heittola, Toni; Huttunen, Heikki; Virtanen, Tuomas

In this paper, the use of multi label neural networks are proposed for detection of temporally overlapping sound events in realistic environments. Real-life sound recordings typically have many overlapping sound events, making it hard to recognize each event with the standard sound event detection methods. Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi label classification in this work. The model is evaluated with recordings from realistic everyday environments and the obtained overall accuracy is 63.8%. The method is compared against a state-of-the-art method using non-negative matrix factorization as a pre-processing stage and hidden Markov models as a classifier. The proposed method improves the accuracy by 19% percentage points overall.


Sound event detection; deep neural networks

Research areas

Book title:
International Joint Conference on Neural Networks 2015 (IJCNN 2015)