Polyphonic Sound Event Detection Using Convolutional Bidirectional Lstm and Synthetic Data-based Transfer Learning

Jung, Sung-Jin; Park, Jung-Bae; Lee, Sangwan

doi:10.1109/icassp.2019.8682909

Cited by 27 publications

(29 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this architecture, Jung et al [90] achieved a single frame segment F1-score of 49.9, which is much higher as compared to Cakir et al [14] architecture producing a single frame segment F1-score of 27.5. With the application of transfer learning, Jung et al [90] achieved an even higher single frame segment F1-score of 55.9. Jung et al [90] system also have a lower single frame ER of 0.56 as compared to Cakir et al [14] architecture, which had a single frame ER of 0.98.…”

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 98%

“…On the other hand, Jung et al [90] proposed using BLSTM to stack with CNN. The architecture and training scheme remains largely similar as compared to [14].…”

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 99%

“…The architecture and training scheme remains largely similar as compared to [14]. For Jung et al [90] architecture, they had more recurrent layers and instead of using batch normalization on the recurrent layers, they applied layer normalization. Since BLSTM produces two outputs that differ due to the input order, the outputs were concatenated together and used as input to the subsequent layer.…”

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 99%

“…Since BLSTM produces two outputs that differ due to the input order, the outputs were concatenated together and used as input to the subsequent layer. As an additional measure to increase the training accuracy, Jung et al [90] proposed the application of transfer learning. It was achieved by training a Convolutional Bidirectional LSTM (CBLSTM) using a set of synthetic data.…”

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Review of Polyphonic Sound Event Detection

Chan

Chin

2020

IEEE Access

View full text Add to dashboard Cite

One of the most amazing functions of the human auditory system is the ability to detect all kinds of sound events in the environment. With the technologies and hardware advances, polyphonic Sound Event Detection (SED) can be developed to mimic the ability of the human auditory system. However, the development of a SED system is no trivial task, and several different factors often hinder accuracy. Although there are several overview papers available, most of them only provide a theoretical overview of algorithms used with little discussion. Thus, to the best of the authors' knowledge, there is no comprehensive review that covers this particular domain. Therefore, this paper aims to provide an in-depth discussion of different methodologies proposed by various authors that include the features used, detection algorithms, and their corresponding accuracy and limitations. Additional information on possible trends is also discussed that can be useful for future development works.

show abstract

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 98%

“…On the other hand, Jung et al [90] proposed using BLSTM to stack with CNN. The architecture and training scheme remains largely similar as compared to [14].…”

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 99%

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 99%

Section: Figure 11 Flowchart Of a Crnnmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Polyphonic Sound Event Detection

Chan

Chin

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Zhao et al [22] constructed one-dimensional CNN-LSTM network and two-dimensional CNN-LSTM network to learned local and global emotion-related features from speech and log Mel spectrogram. Jung et al [23] proposed a new method to improve the performance of polyphonic sound event detection, which combines convolutional bidirectional recurrent neural network (CBRNN) with transfer learning. Passricha et al [24] proposed a CNN-BiLSTM hybrid structure to extract the spatiotemporal features of speech, which could improve the performance of continuous speech recognition.…”

Section: Related Workmentioning

confidence: 99%

An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

ABSTRACRT Since convolutional neural network (CNN) can only extract local features, and long shortterm memory (LSTM) neural network model has a large number of learning calculations, a long processing time and an obvious degree of information loss as the length of speech increases. Utilizing the characteristics of autonomous feature extraction in deep learning, CNN and bidirectional long short-term memory (BiLSTM) network are combined to present an encrypted speech retrieval method based on deep perceptual hashing and CNN-BiLSTM. Firstly, the proposed method extracts the Log-Mel Spectrogram/MFCC features of the original speech and enters the CNN and BiLSTM networks in turn for model training. Secondly, we use the trained fusion network model to learn the deep perceptual feature and generate deep perceptual hashing sequences. Finally, the normalized Hamming distance algorithm is used for matching retrieval. In order to protect the speech security in the cloud, a speech encryption algorithm based on a 4D hyperchaotic system is proposed. The experimental results show that the proposed method has good discrimination, robustness, recall and precision compared with the existing methods, and it has good retrieval efficiency and retrieval accuracy for longer speech. Meanwhile, the proposed speech encryption algorithm has a high key space to resist exhaustive attacks. INDEX TERMS Encrypted speech retrieval, CNN-BiLSTM, Deep perceptual hashing, Speech feature extraction, 4D hyperchaotic system.

show abstract

Exploring current research trends in sound event detection: a systematic literature review

Mohmmad,

Sanampudi

2024

Multimed Tools Appl

View full text Add to dashboard Cite

Polyphonic Sound Event Detection Using Convolutional Bidirectional Lstm and Synthetic Data-based Transfer Learning

Cited by 27 publications

References 17 publications

A Comprehensive Review of Polyphonic Sound Event Detection

A Comprehensive Review of Polyphonic Sound Event Detection

An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM

Exploring current research trends in sound event detection: a systematic literature review

Contact Info

Product

Resources

About