scholarly journals Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) for Sound Event Detection

2019 ◽  
Author(s):  
Teck Kai Chan ◽  
Cheng Siong Chin ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Event Detection ◽  
Matrix Factorization ◽  
Sound Event ◽  
Sound Event Detection ◽  
Non Negative Matrix Factorization

scholarly journals Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection

2021 ◽  
Vol 11 (3) ◽  
pp. 1040
Author(s):  
Seokjin Lee ◽  
Minhan Kim ◽  
Seunghyeon Shin ◽  
Sooyoung Park ◽  
Youngho Jeong
Keyword(s):  
Feature Extraction ◽  
Event Detection ◽  
Matrix Factorization ◽  
Extraction Method ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
Sound Event ◽  
Sound Event Detection ◽  
Weakly Supervised ◽  
Non Negative Matrix Factorization

In this paper, feature extraction methods are developed based on the non-negative matrix factorization (NMF) algorithm to be applied in weakly supervised sound event detection. Recently, the development of various features and systems have been attempted to tackle the problems of acoustic scene classification and sound event detection. However, most of these systems use data-independent spectral features, e.g., Mel-spectrogram, log-Mel-spectrum, and gammatone filterbank. Some data-dependent feature extraction methods, including the NMF-based methods, recently demonstrated the potential to tackle the problems mentioned above for long-term acoustic signals. In this paper, we further develop the recently proposed NMF-based feature extraction method to enable its application in weakly supervised sound event detection. To achieve this goal, we develop a strategy for training the frequency basis matrix using a heterogeneous database consisting of strongly- and weakly-labeled data. Moreover, we develop a non-iterative version of the NMF-based feature extraction method so that the proposed feature extraction method can be applied as a part of the model structure similar to the modern “on-the-fly” transform method for the Mel-spectrogram. To detect the sound events, the temporal basis is calculated using the NMF method and then used as a feature for the mean-teacher-model-based classifier. The results are improved for the event-wise post-processing method. To evaluate the proposed system, simulations of the weakly supervised sound event detection were conducted using the Detection and Classification of Acoustic Scenes and Events 2020 Task 4 database. The results reveal that the proposed system has F1-score performance comparable with the Mel-spectrogram and gammatonegram and exhibits 3–5% better performance than the log-Mel-spectrum and constant-Q transform.

A Survey of Polyphonic Sound Event Detection Based on Non-Negative Matrix Factorization

2016 ◽  
Author(s):  
Manh-Quan Bui ◽  
Viet-Hang Duong ◽  
Seksan Mathulaprangsan ◽  
Bach-Tung Pham ◽  
Wei-Jing Lee ◽  
...  
Keyword(s):  
Event Detection ◽  
Matrix Factorization ◽  
Sound Event ◽  
Sound Event Detection ◽  
Non Negative Matrix Factorization

Neural Network Distillation on IoT Platforms for Sound Event Detection

2019 ◽  
Author(s):  
Gianmarco Cerutti ◽  
Rahul Prasad ◽  
Alessio Brutti ◽  
Elisabetta Farella
Keyword(s):  
Neural Network ◽  
Event Detection ◽  
Sound Event ◽  
Iot Platforms ◽  
Sound Event Detection

scholarly journals Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for \\Weakly Labeled Semi-supervised Sound Event Detection

2021 ◽  
Author(s):  
Janek Ebbers ◽  
Reinhold Haeb-Umbach
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Forward Direction ◽  
Time Step ◽  
Sound Event ◽  
Sound Event Detection ◽  
Validation Set ◽  
Classi Fication

In this paper we present our system for thedetection and classi-fication of acoustic scenes and events (DCASE) 2020 ChallengeTask 4: Sound event detection and separation in domestic envi-ronments. We introduce two new models: the forward-backwardconvolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNNemploys two recurrent neural network (RNN) classifiers sharing thesame CNN for preprocessing. With one RNN processing a record-ing in forward direction and the other in backward direction, thetwo networks are trained to jointly predict audio tags, i.e., weak la-bels, at each time step within a recording, given that at each timestep they have jointly processed the whole recording. The pro-posed training encourages the classifiers to tag events as soon aspossible. Therefore, after training, the networks can be appliedto shorter audio segments of, e.g.,200 ms, allowing sound eventdetection (SED). Further, we propose a tag-conditioned CNN tocomplement SED. It is trained to predict strong labels while using(predicted) tags, i.e., weak labels, as additional input. For train-ing pseudo strong labels from a FBCRNN ensemble are used. Thepresented system scored the fourth and third place in the systemsand teams rankings, respectively. Subsequent improvements allowour system to even outperform the challenge baseline and winnersystems in average by, respectively,18.0 %and2.2 %event-basedF1-score on the validation set. Source code is publicly available athttps://github.com/fgnt/pb_sed

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