Multi-Scale Time-Frequency Attention for Acoustic Event Detection

Zhang, Jingyang; Ding, Wenhao; Kang, Jintao; He, Liang

doi:10.21437/interspeech.2019-1587

Cited by 6 publications

(8 citation statements)

References 17 publications

(38 reference statements)

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“…Compared with other state-of-the-art methods, the performance of our model is competitive. Table 8 and Table 9 show the F1 and ER of the 1D-CRNN [15], TFA [21], MTFA [22], and MTF-CRNN on the T2-dev and T2-eval datasets for three target sound events, respectively. The 1D-CRNN [15] applies a 1-dimensional convolution layer, batch normalization(BN) and a pooling layer, followed by RNN layers and a fully connected layer.…”

Section: ) Test Resultsmentioning

confidence: 99%

“…TFA [21] presented a temporal-frequential attention model for sound event detection, which is an innovative pretrained method with about 260K parameter counts for 3×3, 5×5, and 7×7 convolution kernels that are used. MTFA [22] used a multiscale hourglass structure that outperformed previous single-model methods for rare sound event detection, with a performance that depends on the quantity of annotated data. Our model performs worse than other state-of-the-art methods but has the fewest parameter counts.…”

Section: ) Test Resultsmentioning

confidence: 99%

“…• MTFA: MTFA used a multiscale hourglass structure that outperformed previous single-model methods for rare sound event detection [22].…”

Section: ) Test Resultsmentioning

confidence: 99%

“…Through the experiment, we proved that the performance of the MTF-CRNN is based on a single model with lower parameter counts than the CRNN [11], R-CRNN [19] and MTFA [22]. We first compared the performance of the MTF-CRNN to some single-scale methods on the T2-dev dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Shen et al presented a temporalfrequential attention model for sound event detection, which is an innovative pretrained method [21]. Zhang et al used a multiscale hourglass structure that outperformed previous single-model methods for rare sound event detection [22].…”

Section: Introductionmentioning

confidence: 99%

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MTF-CRNN: Multiscale Time-Frequency Convolutional Recurrent Neural Network for Sound Event Detection

et al. 2020

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To reduce neural network parameter counts and improve sound event detection performance, we propose a multiscale time-frequency convolutional recurrent neural network (MTF-CRNN) for sound event detection. Our goal is to improve sound event detection performance and recognize target sound events with variable duration and different audio backgrounds with low parameter counts. We exploit four groups of parallel and serial convolutional kernels to learn high-level shift-invariant features from the time and frequency domains of acoustic samples. A two-layer bidirectional gated recurrent unit is used to capture the temporal context from the extracted high-level features. The proposed method is evaluated on two different sound event datasets. Compared to that of the baseline method and other methods, the performance is greatly improved as a single model with low parameter counts without pretraining. On the TUT Rare Sound Events 2017 evaluation dataset, our method achieved an error rate (ER) of 0.09±0.01, which was an improvement of 83% compared with the baseline. On the TAU Spatial Sound Events 2019 evaluation dataset, our system achieved an ER of 0.11±0.01, a relative improvement over the baseline of 61%, and F1 and ER values that are better than those of the development dataset. Compared to the state-of-the-art methods, our proposed network achieves competitive detection performance with only one-fifth of the network parameter counts. INDEX TERMS pattern recognition, sound event detection, multiscale learning, time-frequency transform, convolutional recurrent neural network

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Section: ) Test Resultsmentioning

confidence: 99%

Section: ) Test Resultsmentioning

confidence: 99%

“…• MTFA: MTFA used a multiscale hourglass structure that outperformed previous single-model methods for rare sound event detection [22].…”

Section: ) Test Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MTF-CRNN: Multiscale Time-Frequency Convolutional Recurrent Neural Network for Sound Event Detection

et al. 2020

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Gun identification from gunshot audios for secure public places using transformer learning

Nijhawan

Ansari

Kumar

et al. 2022

Sci Rep

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Increased mass shootings and terrorist activities severely impact society mentally and physically. Development of real-time and cost-effective automated weapon detection systems increases a sense of safety in public. Most of the previously proposed methods were vision-based. They visually analyze the presence of a gun in a camera frame. This research focuses on gun-type (rifle, handgun, none) detection based on the audio of its shot. Mel-frequency-based audio features have been used. We compared both convolution-based and fully self-attention-based (transformers) architectures. We found transformer architecture generalizes better on audio features. Experimental results using the proposed transformer methodology on audio clips of gunshots show classification accuracy of 93.87%, with training loss and validation loss of 0.2509 and 0.1991, respectively. Based on experiments, we are convinced that our model can effectively be used as both a standalone system and in association with visual gun-detection systems for better security.

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Sound Event Detection Using Multi-Scale Dense Convolutional Recurrent Neural Network with Lightweight Attention

Yang,

Luo,

Wang

et al. 2023

2023 3rd International Conference on Electronic Information Engineering and Computer (EIECT)

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Multi-Scale Time-Frequency Attention for Acoustic Event Detection

Cited by 6 publications

References 17 publications

MTF-CRNN: Multiscale Time-Frequency Convolutional Recurrent Neural Network for Sound Event Detection

MTF-CRNN: Multiscale Time-Frequency Convolutional Recurrent Neural Network for Sound Event Detection

Gun identification from gunshot audios for secure public places using transformer learning

Sound Event Detection Using Multi-Scale Dense Convolutional Recurrent Neural Network with Lightweight Attention

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