Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features

TL;DR

This paper introduces a 3D CNN-based multichannel sound event detection method that effectively learns inter- and intra-channel features, improving recognition accuracy and training efficiency over single-channel approaches.

Contribution

The paper presents a novel 3D CNN integrated into a CRNN for multichannel SED, demonstrating enhanced performance with fewer training epochs compared to traditional methods.

Findings

Improved F-score by 7.5% with multichannel Ambisonic audio

Reduced error rate by 10% using the proposed method

Recognized 15.6% more overlapping sound events

Abstract

In this paper, we propose a stacked convolutional and recurrent neural network (CRNN) with a 3D convolutional neural network (CNN) in the first layer for the multichannel sound event detection (SED) task. The 3D CNN enables the network to simultaneously learn the inter- and intra-channel features from the input multichannel audio. In order to evaluate the proposed method, multichannel audio datasets with different number of overlapping sound sources are synthesized. Each of this dataset has a four-channel first-order Ambisonic, binaural, and single-channel versions, on which the performance of SED using the proposed method are compared to study the potential of SED using multichannel audio. A similar study is also done with the binaural and single-channel versions of the real-life recording TUT-SED 2017 development dataset. The proposed method learns to recognize overlapping sound events from multichannel features faster and performs better SED with a fewer number of training epochs. The results show that on using multichannel Ambisonic audio in place of single-channel audio we improve the overall F-score by 7.5%, overall error rate by 10% and recognize 15.6% more sound events in time frames with four overlapping sound sources.