Sub-spectrogram Segmentation for Environmental Sound Classification via Convolutional Recurrent Neural Network and Score Level Fusion

Qiao, Tianhao; Zhang, Shunqing; Zhang, Zhichao; Cao, Shan; Xu, Shugong

doi:10.1109/sips47522.2019.9020418

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…Humans are more sensitive to differences at lower frequencies than at higher frequencies. In mathematical terms, the Mel scale is the outcome of a nonlinear transformation of the frequency scale [26]. The term "Melfrequency scale" refers to a scale that is defined as…”

Section: Mel Spectrogrammentioning

confidence: 99%

Speaker Gender Recognition Based on Deep Neural Networks and ResNet50

Alnuaim

Zakariah

Shashidhar³

et al. 2022

Wireless Communications and Mobile Computing

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Several speaker recognition algorithms failed to get the best results because of the wildly varying datasets and feature sets for classification. Gender information helps reduce this effort since categorizing the classes based on gender may help lessen the impact of gender variability on the retrieved features. This study attempted to construct a perfect classification model for language-independent gender identification utilizing the Common Voice dataset (Mozilla). Most previous studies are doing manual extracting characteristics and feeding them into a machine learning model for categorization. Deep neural networks (DNN) were the most effective strategy in our research. Nonetheless, the main goal was to take advantage of the wealth of information included in voice data without requiring significant manual intervention. We trained the deep learning network to choose essential information from speech spectrograms for the classification layer, performing gender detection. The pretrained ResNet 50 fine-tuned gender data successfully achieved an accuracy of 98.57% better than the traditional ML approaches and the previous works reported with the same dataset. Furthermore, the model performs well on additional datasets, demonstrating the approach’s generalization capacity.

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Section: Mel Spectrogrammentioning

confidence: 99%

Speaker Gender Recognition Based on Deep Neural Networks and ResNet50

Alnuaim

Zakariah

Shashidhar³

et al. 2022

Wireless Communications and Mobile Computing

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“…In this paper, a sub-spectrogram segmentation [ 31 ] (Part of this paper has been published in 2019 IEEE International Workshop on Signal Processing Systems) mechanism has been firstly proposed to address the above concerns, which truncates the entire spectrogram into different pieces in order to conduct experiments separately. Score level fusion has been adopted to combine different classification results from different sub-spectrograms.…”

Section: Introductionmentioning

confidence: 99%

High Accurate Environmental Sound Classification: Sub-Spectrogram Segmentation versus Temporal-Frequency Attention Mechanism

Qiao

Zhang

Cao

et al. 2021

Sensors

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In the important and challenging field of environmental sound classification (ESC), a crucial and even decisive factor is the feature representation ability, which can directly affect the accuracy of classification. Therefore, the classification performance often depends to a large extent on whether the effective representative features can be extracted from the environmental sound. In this paper, we firstly propose a sub-spectrogram segmentation with score level fusion based ESC classification framework, and we adopt the proposed convolutional recurrent neural network (CRNN) for improving the classification accuracy. By evaluating numerous truncation schemes, we numerically figure out the optimal number of sub-spectrograms and the corresponding band ranges, and, on this basis, we propose a joint attention mechanism with temporal and frequency attention mechanisms and use the global attention mechanism when generating the attention map. Finally, the numerical results show that the two frameworks we proposed can achieve 82.1% and 86.4% classification accuracy on the public environmental sound dataset ESC-50, respectively, which is equivalent to more than 13.5% improvement over the traditional baseline scheme.

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