Learning Environmental Sounds with Multi-scale Convolutional Neural Network

Zhu, Boqing; Wang, Changjian; Liu, Feng; Jin, Lei; Lu, Zheng; Peng, Yuxing

doi:10.48550/arxiv.1803.10219

Cited by 9 publications

(14 citation statements)

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“…We see that ACRNN outperforms PiczakCNN and obtains an absolute improvement of 13.2% and 21.2% on ESC-10 and ESC-50 datasets, respectively. Then, we compare our model with several state-of-the-art methods: SoundNet8 [1], WaveMsNet [28], EnvNet-v2 [21] and Multi-Stream CNN [12]. We observe that on both ESC-10 and ESC-50 datasets, ACRNN obtains the highest classification accuracy.…”

Section: Modelmentioning

confidence: 98%

“…ESC-10 ESC-50 PiczakCNN [15] 80.5% 64.9% SoundNet [1] 92.1% 74.2% WaveMsNet [28] 93.7% 79.1% EnvNet-v2 [21] 91.4% 84.9% Multi-Stream CNN [12] 93.7% 83.5% ACRNN 93.7% 86.1%…”

Section: Modelmentioning

confidence: 99%

“…We compare our model with existing networks reported as PiczakCNN [15], SoundNet [1], WaveMsNet [28], EnvNet-v2 [21] and Multi-Stream CNN [12]. According to [15], PiczakCNN consists of two convolutional layers and three fully connected layers.…”

Section: Modelmentioning

confidence: 99%

“…We observe that on both ESC-10 and ESC-50 datasets, ACRNN obtains the highest classification accuracy. Note that WaveMsNet [28] and Multi-Stream CNN [12] achieve same classification accuracy as ACRNN on ESC-10 but using feature fusion (raw data and spectrogram features), whereas ACRNN only utilizes spectrogram features.…”

Section: Modelmentioning

confidence: 99%

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Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Zhang

Qiao

et al. 2019

Lecture Notes in Computer Science

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Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose an convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and achieved the state-of-the-art performance in terms of classification accuracy.

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Section: Modelmentioning

confidence: 98%

“…ESC-10 ESC-50 PiczakCNN [15] 80.5% 64.9% SoundNet [1] 92.1% 74.2% WaveMsNet [28] 93.7% 79.1% EnvNet-v2 [21] 91.4% 84.9% Multi-Stream CNN [12] 93.7% 83.5% ACRNN 93.7% 86.1%…”

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

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Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Zhang

Qiao

et al. 2019

Lecture Notes in Computer Science

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“…It is better to explore multiscale discriminative features from a wide range of scales. Although a direct link to multi-scale audio process is based on multi-scale time-frequency resolutions as used in [54], [55], the concept of multi-scale used in our paper is different. The multi-scale in this paper refers to multi-scale of spectral patches processed in a DCNN model.…”

Section: E Experiments On Dcase2017 T4 Corpusmentioning

confidence: 99%

Cross-scale Attention Model for Acoustic Event Classification

Lu,

Shen,

et al. 2019

Preprint

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Convolutional neural network (CNN) is an indispensable building block for designing a state of the art system for acoustic event classification (AEC). By stacking multiple CNN layers, the model could explore long-range dependency of explored features in top layers with increasing of feature abstraction. However it is also possible that the discriminative features with short-range dependency which are distributed locally are smooth out in the final representation. In this paper, we propose a progressive multi-scale attention (MSA) model which explicitly integrates multi-scale features with short-and longrange dependency in feature extraction. Based on mathematic formulations, we revealed that the conventional residual CNN (ResCNN) model could be explained as a special case of the proposed MSA model, and the MSA model could use the ResCNN as a backbone with an attentive feature weighting in consecutive scales. The discriminative features in multi-scales are progressively propagated to top layers for the final representation. Therefore, the final representation encodes multi-scale features with local and global discriminative structures which are expected to improve the performance. We tested the proposed model on two AEC data corpora, one is for urban acoustic event classification task, the other is for acoustic event detection in smart car environments. Our results showed that the proposed MSA model effectively improved the performance on the current state-of-the-art deep learning algorithms.

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Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features

Zhu

Wang

et al. 2018

Lecture Notes in Computer Science

Self Cite

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Motivated by the fact that characteristics of different sound classes are highly diverse in different temporal scales and hierarchical levels, a novel deep convolutional neural network (CNN) architecture is proposed for the environmental sound classification task. This network architecture takes raw waveforms as input, and a set of separated parallel CNNs are utilized with different convolutional filter sizes and strides, in order to learn feature representations with multi-temporal resolutions. On the other hand, the proposed architecture also aggregates hierarchical features from multi-level CNN layers for classification using direct connections between convolutional layers, which is beyond the typical single-level CNN features employed by the majority of previous studies. This network architecture also improves the flow of information and avoids vanishing gradient problem. The combination of multi-level features boosts the classification performance significantly. Comparative experiments are conducted on two datasets: the environmental sound classification dataset (ESC-50), and DCASE 2017 audio scene classification dataset. Results demonstrate that the proposed method is highly effective in the classification tasks by employing multi-temporal resolution and multi-level features, and it outperforms the previous methods which only account for single-level features.

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Learning Environmental Sounds with Multi-scale Convolutional Neural Network

Cited by 9 publications

References 0 publications

Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Cross-scale Attention Model for Acoustic Event Classification

Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features

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