Learning Environmental Sounds with Multi-scale Convolutional Neural Network

Zhu, Boqing; Wang, Changjian; Liu, Feng; Jin, Lei; Huang, Zhen; Peng, Yuxing; Li, Fēi

doi:10.1109/ijcnn.2018.8489641

Cited by 59 publications

(36 citation statements)

References 25 publications

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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: 1%mentioning

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%…”

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: 1%mentioning

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: 1%mentioning

confidence: 99%

See 3 more Smart Citations

Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Zhang

Qiao

et al. 2019

Pattern Recognition and Computer Vision

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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: 1%mentioning

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%…”

Section: Modelmentioning

confidence: 99%

Section: 1%mentioning

confidence: 99%

Section: 1%mentioning

confidence: 99%

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Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Zhang

Qiao

et al. 2019

Pattern Recognition and Computer Vision

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“…More recent research has experimented with features at different temporal scales by merging the RNN outputs overtime in a stacked RNN architecture [12] and with modifying spatial resolution by applying different filters to the input [13]. Other approaches towards improving ESC performance include higher-level input features such as MFCCs, gammatone features, or specialized filters [4,13] and training with data augmentation [3,14]. Multiple loss functions were used for the detection of rare sound events in [12].…”

Section: Related Workmentioning

confidence: 99%

Multi-Stream Network with Temporal Attention for Environmental Sound Classification

Chebiyyam

Kirchhoff

2019

Interspeech 2019

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Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three commonly used data sets for environmental sound and audio scene classification and achieve new state-of-the-art performance without any changes in network architecture or frontend preprocessing, thus demonstrating better generalizability.

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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

Advances in Multimedia Information Processing – PCM 2018

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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 59 publications

References 25 publications

Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Attention Based Convolutional Recurrent Neural Network for Environmental Sound Classification

Multi-Stream Network with Temporal Attention for Environmental Sound Classification

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

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