Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF

Zhou, Qing; Feng, Zuren; Benetos, Emmanouil

doi:10.3390/s19143206

Cited by 24 publications

(19 citation statements)

References 31 publications

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“…Whereas, for Pooling Size in Table 9, the value indicates the pooling size at each layer. VOLUME XX, 2017 9 Thus, a pooling size of (1, 5), (1,4), (1,2) represent that the T-F input only has its frequency dimension reduced by 5 times in the first layer, further reduced by another 4 times in the second layer and another 2 times in the last layer. Using a 40 mel bands T-F input as an example, the 40 bands become 1 band in 3 stages: 40 → 8 → 2 → 1.…”

Section: Summary Of Hybrid Modelsmentioning

confidence: 99%

“…After a specific training epoch, the training procedure transit into the second stage where the combined cost was added with another component, 4 C which can be represented as…”

Section: Figure 14 Flowchart Of Lin Et Al Framework [114]mentioning

confidence: 99%

“…Thirdly, some events can only be detected by a SED system such as a car horn. Finally, sound requires lesser computational resources as compared to an image or video [3], [4]. Thus, a SED system has great potential in many other domains besides speech recognition [5], [6] such as medical telemonitoring [7], surveillance [8], [9], equipment monitoring [10] [11] and wildlife monitoring [12], [13].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review of Polyphonic Sound Event Detection

Chan

Chin

2020

IEEE Access

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One of the most amazing functions of the human auditory system is the ability to detect all kinds of sound events in the environment. With the technologies and hardware advances, polyphonic Sound Event Detection (SED) can be developed to mimic the ability of the human auditory system. However, the development of a SED system is no trivial task, and several different factors often hinder accuracy. Although there are several overview papers available, most of them only provide a theoretical overview of algorithms used with little discussion. Thus, to the best of the authors' knowledge, there is no comprehensive review that covers this particular domain. Therefore, this paper aims to provide an in-depth discussion of different methodologies proposed by various authors that include the features used, detection algorithms, and their corresponding accuracy and limitations. Additional information on possible trends is also discussed that can be useful for future development works.

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Section: Summary Of Hybrid Modelsmentioning

confidence: 99%

“…After a specific training epoch, the training procedure transit into the second stage where the combined cost was added with another component, 4 C which can be represented as…”

Section: Figure 14 Flowchart Of Lin Et Al Framework [114]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review of Polyphonic Sound Event Detection

Chan

Chin

2020

IEEE Access

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“…NMF aims at finding two non-negative matrix

and

to roughly approximate the original data matrix, i.e.,

. Since many real-world datasets are usually high-dimensional, the NMF methods have been widely applied in image analysis [ 40 ], data mining, speech denoising [ 41 ] and population genetics, etc. The semi-NMF [ 42 ] is an extension of traditional NMF, which only requires the coefficient matrix to be non-negative.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

Zhang

Wang

et al. 2020

Sensors

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With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method.

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“…Sound event classification (SEC) has vastly been exploited by many researchers. Sound can be categorized into speech, music, noise, environmental sound, or daily living sound [26]. Sound events are available in all classes, for example, car horn, traffic, walking, or knocking, etc.…”

Section: Introductionmentioning

confidence: 99%

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Parathai

Tengtrairat

Woo

et al. 2020

Sensors

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This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.

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Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF

Cited by 24 publications

References 31 publications

A Comprehensive Review of Polyphonic Sound Event Detection

A Comprehensive Review of Polyphonic Sound Event Detection

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

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