Dynamic Time Warping (DTW) is a time domain based method and widely used in various similar recognition and data mining applications. This paper presents a phase compensation based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align up two signals f...

Dynamic time warping (DTW) is an algorithm to find out the similarity between two temporal sequences of varying length. Previous works in this field can be traced back to as early as [1], for automatic speech recognition (ASR). Although this technique became obsolete for ASR with the advent of Hidden Markov Models (HMM) [2] and Deep Neural Network (DNN) based hybrid models [3], [4], DTW was fou...

Automatic phrase detection systems of bird sounds are useful in several applications as they reduce the need for manual annotations. However, birdphrase detection is challenging due to limited training data and background noise. Limited data occur because of limited recordings or the existence of rare phrases. Background noise interference occurs because of the intrinsic nature of the recording...

Speaker verification from talking a few words of sentences has many applications. Many methods as DTW, HMM, VQ and MQ can be used for speaker verification. We applied MQ for its precise, reliable and robust performance with computational simplicity. We also used pitch frequency and log gain contour for further improvement of the system performance.

Dear Editor, This letter proposes a new pattern matching method based on word embedding and dynamic time warping (DTW) to identify groups of similar alarm floods. First, messages are transformed into numeric values that represent alarms also reflect the relationships between occurrences. Then, similarities numerically encoded flood sequences calculated by DTW floods identified via clustering. T...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...

In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patter...