A Comprehensive Review of Polyphonic Sound Event Detection

Chan, Teck Kai; Chin, Cheng Siong

doi:10.1109/access.2020.2999388

Cited by 38 publications

(20 citation statements)

References 108 publications

(249 reference statements)

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“…8: Affine mapping (3) based on sliding source along table edge (points 1-2-3-4) using point 1 as reference and (b) long-table edge (points 5-6) using point 6 as reference. The estimated B matrix is based on table (point 1-6), chair (point 7-11), and chair+table (point [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Mappingmentioning

confidence: 99%

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Audio scene monitoring using redundant ad-hoc microphone array networks

Gerstoft,

Hu,

Bianco

et al. 2021

Preprint

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We present a system for localizing sound sources in a room with several microphone arrays. Unlike most existing approaches, the positions of the arrays in space are assumed to be unknown. Each circular array performs direction of arrival (DOA) estimation independently. The DOAs are then fed to a fusion center where they are concatenated and used to perform the localization based on two proposed methods, which require only few labeled source locations for calibration. The first proposed method is based on principal component analysis (PCA) of the observed DOA and does not require any calibration. The array cluster can then perform localization on a manifold defined by the PCA of concatenated DOAs over time. The second proposed method performs localization using an affine transformation between the DOA vectors and the room manifold. The PCA approach has fewer requirements on the training sequence, but is less robust to missing DOAs from one of the arrays. The approach is demonstrated with a set of five 8-microphone circular arrays, placed at unknown fixed locations in an office. Both the PCA approach and the direct approach can easily map out a rectangle based on a few calibration points with similar accuracy as calibration points. The methods demonstrated here provide a step towards monitoring activities in a smart home and require little installation effort as the array locations are not needed.

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Section: Mappingmentioning

confidence: 99%

“…This could be used for fall monitoring [4] and daily events classification [5]. This system could also be combined to sound scene classification, e.g., [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Audio scene monitoring using redundant ad-hoc microphone array networks

Gerstoft,

Hu,

Bianco

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…inside a house or in a crowded street) or to different actions or sources which produced the obtained acoustic signals. In the former case, we talk about acoustic scene classification [8], while in the latter the problem at hand is sound event classification or detection [9].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Resolution CRNN-Based Approach for Semi-Supervised Sound Event Detection in DCASE 2020 Challenge

2021

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Sound Event Detection is a task with a rising relevance over the recent years in the field of audio signal processing, due to the creation of specific datasets such as Google AudioSet or DESED (Domestic Environment Sound Event Detection) and the introduction of competitive evaluations like the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). The different categories of acoustic events can present diverse temporal and spectral characteristics. However, most approaches use a fixed time-frequency resolution to represent the audio segments. This work proposes a multi-resolution analysis for feature extraction in Sound Event Detection, hypothesizing that different resolutions can be more adequate for the detection of different sound event categories, and that combining the information provided by multiple resolutions could improve the performance of Sound Event Detection systems. Experiments are carried out over the DESED dataset in the context of the DCASE 2020 Challenge, concluding that the combination of up to 5 resolutions allows a neural network-based system to obtain better results than single-resolution models in terms of event-based F1-score in every event category and in terms of PSDS (Polyphonic Sound Detection Score). Furthermore, we analyze the impact of score thresholding in the computation of F1-score results, finding that the standard value of 0.5 is suboptimal and proposing an alternative strategy based in the use of a specific threshold for each event category, which obtains further improvements in performance.

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“…Such a problem is more likely to be solved when one had access to a large corpus of strongly labeled data where the event tags and corresponding onsets and offsets are known with certainty. However, the lack of strongly labeled data is a recurring problem faced during model development as it is difficult and time-consuming to collect [4].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised NMF-CNN for Sound Event Detection

Chan

Chin

2021

IEEE Access

Self Cite

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The lack of strongly labeled data can limit the potential of a Sound Event Detection (SED) system trained using deep learning approaches. To address this issue, this paper proposes a novel method to approximate strong labels for the weakly labeled data using Nonnegative Matrix Factorization (NMF) in a supervised manner. Using a combinative transfer learning and semi-supervised learning framework, two different Convolutional Neural Networks (CNN) are trained using synthetic data, approximated strongly labeled data, and unlabeled data where one model will produce the audio tags. In contrast, the other will produce the frame-level prediction. The proposed methodology is then evaluated on three different subsets of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 dataset: validation dataset, challenge evaluation dataset, and public YouTube evaluation dataset. Based on the results, our proposed methodology outperforms the baseline system by a minimum of 7% across these three different data subsets. In addition, our proposed method also outperforms the top 3 submissions from the DCASE 2019 challenge task 4 on the validation and public YouTube evaluation datasets. Our system performance is also competitive against the top submission in DCASE 2020 challenge task 4 on the challenge evaluation data. A post-challenge analysis was also performed using the validation dataset, which revealed the causes of the performance difference between our system and the top submission of the DCASE 2020 challenge task 4. The leading causes that we observed are 1) detection threshold tuning method and 2) augmentation techniques used. We observed that our system could perform better than the first place submission by 1.5% by changing our detection threshold tuning method. In addition, the post-challenge analysis also revealed that our system is more robust than the top submission in DCASE 2020 challenge task 4 on long-duration audio clips, where we outperformed them by 37%.

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A Comprehensive Review of Polyphonic Sound Event Detection

Cited by 38 publications

References 108 publications

Audio scene monitoring using redundant ad-hoc microphone array networks

Audio scene monitoring using redundant ad-hoc microphone array networks

A Multi-Resolution CRNN-Based Approach for Semi-Supervised Sound Event Detection in DCASE 2020 Challenge

Semi-Supervised NMF-CNN for Sound Event Detection

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