Convolutional bidirectional long short-term memory hidden Markov model hybrid system for polyphonic sound event detection

<p>This paper presents a review of anomalous sound event detection(SED) approaches. SED is becoming more applicable for real-world appliactaions such as security, fire determination or olther emergency alarms. Despite many research outcome previously, further research is required to reduce false positives and improve accurracy. SED approaches are comprehensively organized by methods covering system pipeline components of acoustic descriptors, classification engine, and decision finalization method. The review compares multiple approaches that is applied on a specific dataset. Security relies on anomalous events in order to prevent it one must find these anomalous events. Audio surveillance has become more efficient as that artificial intelligence has stepped up the game. Autonomous SED could be used for early detection and prevention. It is found that the state of the art method viable used in SED using features of log-mel energies in convolutional recurrent neural network(CRNN) with long short term memory(LSTM) with a verification step of thresholding has obtained 93.1% F1 score and 0.1307 ER. It is found that feature extraction of log mel energies are highly reliable method showing promising results on multiple experiments.</p>

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A data-driven approach based on long short-term memory and hidden Markov model for crack propagation prediction

Engineering Fracture Mechanics ◽

10.1016/j.engfracmech.2020.107085 ◽

2020 ◽

Vol 235 ◽

pp. 107085 ◽

Cited By ~ 1

Author(s):

Duyen H. Nguyen-Le ◽

Q.B. Tao ◽

Vu-Hieu Nguyen ◽

Magd Abdel-Wahab ◽

H. Nguyen-Xuan

Keyword(s):

Markov Model ◽

Crack Propagation ◽

Short Term Memory ◽

Hidden Markov ◽

Data Driven ◽

Short Term ◽

Term Memory ◽

Data Driven Approach ◽

Long Short Term Memory ◽

Propagation Prediction

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Two-Stream Bidirectional Long Short-Term Memory for Mitosis Event Detection and Stage Localization in Phase-Contrast Microscopy Images

Lecture Notes in Computer Science - Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017 ◽

10.1007/978-3-319-66185-8_7 ◽

2017 ◽

pp. 56-64 ◽

Cited By ~ 5

Author(s):

Yunxiang Mao ◽

Zhaozheng Yin

Keyword(s):

Event Detection ◽

Phase Contrast ◽

Short Term Memory ◽

Phase Contrast Microscopy ◽

Short Term ◽

Term Memory ◽

Contrast Microscopy ◽

Long Short Term Memory ◽

Microscopy Images

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LSTM-Based with Deterministic Negative Sampling for API Suggestion

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194019500347 ◽

2019 ◽

Vol 29 (07) ◽

pp. 1029-1051 ◽

Cited By ~ 2

Author(s):

Jinpei Yan ◽

Yong Qi ◽

Qifan Rao ◽

Hui He ◽

Saiyu Qi

Keyword(s):

Short Term Memory ◽

Hidden Markov ◽

Specific Model ◽

Great Effort ◽

Short Term ◽

Term Memory ◽

Computation Efficiency ◽

Long Short Term Memory ◽

N Gram ◽

Lstm Network

Modern programming relies on a large number of fundamental APIs, but programmers often take great effort to remember names and the usage of APIs when coding, and repeatedly search the related API documents or Q&A websites (e.g. Stack Overflow). To improve the programming efficiency, we present a Java API suggestion model called APIHelper which learns API sequence pattern via the Long Short-Term Memory (LSTM) network, then provides API suggestion based on the program context. Comparing with statistical methods (e.g. Hidden Markov Model (HMM), N-gram), which require establishing one specific model for each class, we propose Deterministic Negative Sampling (DNS) to make API suggestion for a large number of Java classes by one single end-to-end LSTM. To verify this approach, we make API suggestion for 50,000 Java classes and evaluate it with accuracy and top-K accuracy. The results show that APIHelper outperforms other research works both on accuracy and computation efficiency.

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