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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
TLDR
This paper proposes pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset, and investigates the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks.
View on IEEE
Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy
TLDR
Experimental results show that the proposed two-stage polyphonic sound event detection and localization method is able to improve the performance of both SED and DOAE, and also performs significantly better than the baseline method.
View PDF on arXiv
Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems
TLDR
This paper proposes generic cross-task baseline systems based on convolutional neural networks (CNNs) and finds that the 9-layer CNN with average pooling is a good model for a majority of the DCASE 2019 tasks.
View PDF on arXiv
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
TLDR
The proposed EINV2 for joint SED and DoA estimation outperforms previous methods by a large margin, and has comparable performance to state-of-the-art ensemble models.
View on IEEE
Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation
TLDR
This work proposes to estimate phases by estimating complex ideal ratio masks (cIRMs) where it decouple the estimation of cIRMs into magnitude and phase estimations, and extends the separation method to effectively allow the magnitude of the mask to be larger than 1.
View PDF on arXiv
Source Separation with Weakly Labelled Data: an Approach to Computational Auditory Scene Analysis
TLDR
This work proposes a source separation framework trained with weakly labelled data that can separate 527 kinds of sound classes from AudioSet within a single system.
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TWO-STAGE SOUND EVENT LOCALIZATION AND DETECTION USING INTENSITY VECTOR AND GENERALIZED CROSS-CORRELATION Technical Report
TLDR
A two-stage polyphonic sound event detection and localization method that is able to localize and detect overlapping sound events in different environments, and can improve the performance of both SED and DOA estimation, and performs significantly better than the baseline method.
INCORPORATING AUXILIARY DATA FOR URBAN SOUND TAGGING Technical Report
TLDR
A feature vector is constructed based on the spatiotemporal metadata and used in parallel with log-mel spectrogram features to facilitate sound tagging and the presence of multiple annotations per recording is addressed by using a pseudo-labelling technique.
Learning With Out-of-Distribution Data for Audio Classification
TLDR
It is shown that detecting and relabelling certain OOD instances, rather than discarding them, can have a positive effect on learning, and an instance of labelling error for classification tasks in which the dataset is corrupted with out-of-distribution (OOD) instances is investigated.
View on IEEE
Generalisation in Environmental Sound Classification: The ‘Making Sense of Sounds’ Data Set and Challenge
TLDR
A baseline classification system is introduced, a deep convolutional network, which showed strong performance with an average accuracy on the evaluation data, and is discussed in the light of two alternative explanations: An unlikely accidental category bias in the sound recordings or a more plausible true acoustic grounding of the high-level categories.
View on IEEE
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