General audio tagging with ensembling convolutional neural network and statistical features

Abstract

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample re-weight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Our codes are available at https://github.com/Cocoxili/DCASE2018Task2/.

Submitted to ICASSP

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• mixup: Beyond Empirical Risk Minimization
• Squeeze-and-Excitation Networks
• Densely Connected Convolutional Networks
• General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
• Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
• Dual Path Networks
• A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
• Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network

Cited by in corpus (2)

• Spoken Language Identification using ConvNets
• AudioSet-R: A Refined AudioSet with Multi-Stage LLM Label Reannotation