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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

Kong, Qiuqiang, Cao, Yin, Iqbal, Turab, Wang, Yuxuan, Wang, Wenwu and Plumbley, Mark D (2020) PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28. pp. 2880-2894.

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—Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification , speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited durations. Recently, in computer vision and natural language processing, systems pretrained on large-scale datasets have generalized well to several tasks. However, there is limited research on pretraining systems on large-scale datasets for audio pattern recognition. In this paper, we propose pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature. Our best PANN system achieves a state-of-the-art mean average precision (mAP) of 0.439 on AudioSet tagging, outperforming the best previous system of 0.392. We transfer PANNs to six audio pattern recognition tasks, and demonstrate state-of-the-art performance in several of those tasks. We have released the source code and pretrained models of PANNs:

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Wang, Yuxuan
Plumbley, Mark
Date : 19 October 2020
DOI : 10.1109/TASLP.2020.3030497
Copyright Disclaimer : © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Christine Daoutis
Date Deposited : 17 Nov 2020 11:53
Last Modified : 17 Nov 2020 11:53

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