http://hdl.handle.net/2451/60387 2026-04-03T22:40:59Z http://hdl.handle.net/2451/60777 Title: Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) for Sound Event Detection Authors: Chan, Teck Kai; Chin, Cheng Siong; Li, Ye Abstract: The main scientific question of this year DCASE chal-lenge, Task 4 - Sound Event Detection in Domestic Environments, is to investigate the types of data (strongly labeled synthetic data, weakly labeled data, unlabeled in domain data) required to achieve the best performing system. In this paper, we proposed a deep learning model that integrates Non-Negative Matrix Factorization (NMF) with Convolutional Neural Net-work (CNN). The key idea of such integration is to use NMF to provide an approximate strong label to the weakly labeled data. Such integration was able to achieve a higher event based F1-score as compared to the baseline system (Evaluation Dataset: 30.39% vs. 23.7%, Validation Dataset: 31% vs. 25.8%). By com-paring the validation results with other participants, the proposed system was ranked 8th among 19 teams (inclusive of the baseline system) in this year Task 4 challenge. 2019-10-01T00:00:00Z http://hdl.handle.net/2451/60776 Title: SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network Authors: Cartwright, Mark; Mendez, Ana Elisa Mendez; Cramer, Aurora; Lostanlen, Vincent; Dove, Graham; Wu, Ho-Hsiang; Salamon, Justin; Nov, Oded; Bello, Juan Abstract: SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for real-world urban noise monitoring. It consists of 3068 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network. Via the Zooniverse citizen science platform, volunteers tagged the presence of 23 fine-grained classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into eight coarse-grained classes. In this work, we describe the collection of this dataset, metrics used to evaluate tagging systems, and the results of a simple baseline model. 2019-10-01T00:00:00Z http://hdl.handle.net/2451/60774 Title: DCASE 2019 Task 2: Multitask Learning, Semi-supervised Learning and Model Ensemble with Noisy Data for Audio Tagging Authors: Akiyama, Osamu; Sato, Junya Abstract: This paper describes our approach to the DCASE 2019 challenge Task 2: Audio tagging with noisy labels and minimal supervision. This task is a multi-label audio classification with 80 classes. The training data is composed of a small amount of reliably labeled data (curated data) and a larger amount of data with unreliable labels (noisy data). Additionally, there is a difference in data distribution between curated data and noisy data. To tackle this difficulty, we propose three strategies. The first is multitask learning using noisy data. The second is semi-supervised learning using noisy data and labels that are relabeled using trained models’ predictions. The third is an ensemble method that averages models trained with different time length. By using these methods, our solution was ranked in 3rd place on the public leaderboard (LB) with a label-weighted label-ranking average precision (lwlrap) score of 0.750 and ranked in 4th place on the private LB with a lwlrap score of 0.75787. The code of our solution is available at https://github.com/OsciiArt/Freesound-Audio-Tagging-2019. 2019-10-01T00:00:00Z http://hdl.handle.net/2451/60775 Title: Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy Authors: Cao, Yin; Kong, Qiuqiang; Iqbal, Turab; An, Fengyan; Wang, Wenwu; Plumbley, Mark Abstract: Sound event detection (SED) and localization refer to recognizing sound events and estimating their spatial and temporal locations. Using neural networks has become the prevailing method for SED. In the area of sound localization, which is usually performed by estimating the direction of arrival (DOA), learning-based methods have recently been developed. In this paper, it is experimentally shown that the trained SED model is able to contribute to the direction of arrival estimation (DOAE). However, joint training of SED and DOAE degrades the performance of both. Based on these results, a two-stage polyphonic sound event detection and localization method is proposed. The method learns SED first, after which the learned feature layers are transferred for DOAE. It then uses the SED ground truth as a mask to train DOAE. The proposed method is evaluated on the DCASE 2019 Task 3 dataset, which contains different overlapping sound events in different environments. Experimental results show that the proposed method is able to improve the performance of both SED and DOAE, and also performs significantly better than the baseline method. 2019-10-01T00:00:00Z