Polyphonic Sound Event Detection Using Modified Recurrent Temporal Pyramid Neural Network

In this paper, a novel approach to performing polyphonic Sound Event Detection (SED) is presented. A new deep learning architecture named "Modified Recurrent Temporal Pyramid Neural Network (MR-TPNN)" is introduced. The input features fed to the network are spectrograms generated from Constant Q-Transform (CQT). CQT spectrograms provided better sound event information in the audio recording than the Short Time Fourier Transform (STFT) and Fast Fourier Transform (FFT) methods. The temporal information is an essential factor for detecting the onset and offset of events in an audio recording. Capturing the temporal information is ensured by fusing Temporal pyramids and Bi-directional long short-term memory (LSTM) recurrent layers in deep learning architecture. Extensive experiments are carried out on three benchmark datasets, and the results of the proposed method are superior to those of the existing polyphonic SED systems.