Acoustic event detection in polyphonic environments using artificial neural networks

Abstract

Our environment is a mixture of hundreds of sounds that are emitted by different sound sources. These sounds are overlapped in both time and frequency domains in an unstructured manner composing a polyphonic environment. Identification of acoustic events in a polyphonic environment has become an emerging topic with many applications such as surveillance, context-aware computing, automatic audio indexing, health care monitoring and bioacoustics monitoring. Polyphonic acoustic event detection is a challenging task aimed at detecting the presence of multiple sound events that are overlapped at a particular time instance and labeling. It requires a large amount of training data with a complex machine learning architecture thus making it a highly resource-consuming task. Hence, the accuracy of this research area is still not at a satisfactory level. This study presents a neural networks-based classifier architecture with data augmentation and post-processing methods to improve accuracy. Two neural network architectures as a multi-label and combined single label are implemented and compared in the study. Previous literature reveals that Mel frequency cepstral coefficients and log Mel-band energies are the widely used features in the state of the art research in the area. Different data augmentation methods were used to ensure that the neural networks are trained for even the slight variations of the environmental sounds. A novel binarization method based on the signal energy is proposed to calculate the threshold value for binarizing the source presence predictions. Finally, the median filter based post processing was implemented to smoothen the detection results. The experimental results show that the proposed binarizing method improved the detection accuracy and recorded a maximum of 62.5% combined with the data augmentation and post-processing.