Acoustic Scene Classification Using Deep Audio Feature and BLSTM Network

Li, Yanxiong; Li, Xianku; Zhang, Yuhan; Wang, Wucheng; Liu, Mingle; Feng, Xiaohui

doi:10.1109/icalip.2018.8455765

Cited by 22 publications

(4 citation statements)

References 19 publications

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“…Yong Xu et al [21] presented a gated Convolutional Neural Network that won the 1st place in the large-scale weakly supervised sound event detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. Yan Xiong Li et al [22] used the BLSTM Network on Acoustic Scenes to get a better result. Kele Xu et al [23] purpose a novel ensemble-learning system consists of CNN that gets a superior classification performance.…”

Section: Related Workmentioning

confidence: 99%

Heartbeat Sound Signal Classification Using Deep Learning

Raza

Mehmood

Ullah

et al. 2019

Sensors

121

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Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

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Section: Related Workmentioning

confidence: 99%

Heartbeat Sound Signal Classification Using Deep Learning

Raza

Mehmood

Ullah

et al. 2019

Sensors

121

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“…In addition to basic time-frequency transformations, perceptually-motivated signal representations are used as input to deep neural networks. Such representations for instance characterize the distribution (e.g., Mel-frequency cepstral coefficients (MFCC) [17], sub-band power distribution [18], and gammatone frequency cepstral coefficients [19]) and modulation of the spectral energy (e.g., amplitude modulation bank features [20] and temporal energy variation [21]). Feature learning techniques based on hand-crafted audio features and traditional classification algorithms such as support vector machines (SVM) have been shown to underperform deep learning based ASC algorithms [22,23].…”

Section: Fixed Signal Transformationsmentioning

confidence: 99%

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Abeßer

2020

Applied Sciences

125

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The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.

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“…These vectors must maintain the most important information while removing extraneous acoustic information. To begin, MFCCs were used to evaluate the results [14,15]. A thorough decompression of the sound is required.…”

Section: Schematics For Parameterisationmentioning

confidence: 99%

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2021

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Acoustic Scene Classification Using Deep Audio Feature and BLSTM Network

Cited by 22 publications

References 19 publications

Heartbeat Sound Signal Classification Using Deep Learning

Heartbeat Sound Signal Classification Using Deep Learning

A Review of Deep Learning Based Methods for Acoustic Scene Classification

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