scholarly journals ClefNet: Recurrent Autoencoders with Dynamic Time Warping for Near-Lossless Music Compression and Minimal-Latency Transmission

2021 ◽  
Author(s):  
Vignav Ramesh ◽  
Mason Wang
Keyword(s):  
Real Time ◽  
Dynamic Time Warping ◽  
Target Sequence ◽  
Time Warping ◽  
Music Production ◽  
Latent Space ◽  
Street Traffic ◽  
Dynamic Time ◽  
Evaluation Metric ◽  
Client Side

The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change. Due to the public health guidelines imposed during the COVID-19 pandemic, there is no longer sufficient street traffic for remaining buskers to generate sufficient revenue, leading a majority of street musicians to pursue remote music production. However, real-time music production is notoriously difficult due to the excessively high latencies that current video call platforms such as Zoom and Google Meet harbor. In this paper, we propose an architecture for a platform with end-to-end, near-lossless audio transmission tailored specifically to online joint music production, called Latent Space. We discuss the usage of a recurrent autoencoder with sequence-aware encoding (RAES) and a 1D convolutional layer for audio compression, which we dub ClefNet, as well as propose a new evaluation metric for naive autoencoders (AEs), MSE-DTW loss, which combines the traditional mean square error (MSE) loss function with dynamic time warping (DTW) to prevent an increase in loss when the target sequence predicted by the AE is strictly a temporal variation of the source sequence. Moreover, we detail the logistics of a live system implementation which uses the Web Audio API to extract raw audio samples in real-time to feed into our client-side model before relaying the traffic using peer-to-peer WebRTC technology. The Latent Space platform can be accessed at https://latent-space.tech, and the code and data can be found under the MIT License at https://github.com/rvignav/ClefNet.

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

2022 ◽  
Vol 64 (1) ◽  
pp. 38-44
Author(s):  
Maosheng Gao ◽  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Yan Yu
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Time Warping ◽  
Trend Prediction ◽  
Residual Signal ◽  
Dynamic Time

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

scholarly journals Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

2018 ◽  
Author(s):  
Sang Hyuk Kim ◽  
Hee Soo Lee ◽  
Hanjun Ko ◽  
Seung Hwan Jeong ◽  
Hyun Woo Byun ◽  
...  
Keyword(s):  
Financial Markets ◽  
Real Time ◽  
Pattern Matching ◽  
Dynamic Time Warping ◽  
Series Data ◽  
Time Warping ◽  
Trading System ◽  
Index Futures ◽  
Data Movement ◽  
Dynamic Time

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the pattern of KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Investor communities that have sustained financial markets are able to make more efficient investments by using the PMTS. In this sense, the system developed in this paper is a sustainable investment technique and helps financial markets achieve efficient sustainability.

scholarly journals Real time selective sequencing using nanopore technology.

2016 ◽  
Author(s):  
Matthew Loose ◽  
Sunir Malla ◽  
Michael Stout
Keyword(s):  
Real Time ◽  
Dynamic Time Warping ◽  
Current Flow ◽  
Direct Selection ◽  
Dna Molecules ◽  
Time Warping ◽  
Oxford Nanopore ◽  
Flow Through ◽  
Dynamic Time ◽  
Selection Of

The Oxford Nanopore MinION is a portable real time sequencing device which functions by sensing the change in current flow through a nanopore as DNA passes through it. These current values can be streamed in real time from individual nanopores as DNA molecules traverse them. Furthermore, the technology enables individual DNA molecules to be rejected on demand by reversing the voltage across specific channels. In theory, combining these features enables selection of individual DNA molecules for sequencing from a pool, an approach called "Read Until". Here we apply dynamic time warping to match short query current traces to references, demonstrating selection of specific regions of small genomes, individual amplicons from a group of targets, or normalisation of amplicons in a set. This is the first demonstration of direct selection of specific DNA molecules in real time whilst sequencing on any device and enables many novel uses for the MinION.

scholarly journals Real-Time Musical Conducting Gesture Recognition Based on a Dynamic Time Warping Classifier Using a Single-Depth Camera

2019 ◽  
Vol 9 (3) ◽  
pp. 528 ◽  
Author(s):  
Fahn Chin-Shyurng ◽  
Shih-En Lee ◽  
Meng-Luen Wu
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Recognition System ◽  
Experimental Result ◽  
Depth Camera ◽  
Time Warping ◽  
Skeleton Model ◽  
Time Dynamic ◽  
Dynamic Time

Gesture recognition is a human−computer interaction method, which is widely used for educational, medical, and entertainment purposes. Humans also use gestures to communicate with each other, and musical conducting uses gestures in this way. In musical conducting, conductors wave their hands to control the speed and strength of the music played. However, beginners may have a limited comprehension of the gestures and might not be able to properly follow the ensembles. Therefore, this paper proposes a real-time musical conducting gesture recognition system to help music players improve their performance. We used a single-depth camera to capture image inputs and establish a real-time dynamic gesture recognition system. The Kinect software development kit created a skeleton model by capturing the palm position. Different palm gestures were collected to develop training templates for musical conducting. The dynamic time warping algorithm was applied to recognize the different conducting gestures at various conducting speeds, thereby achieving real-time dynamic musical conducting gesture recognition. In the experiment, we used 5600 examples of three basic types of musical conducting gestures, including seven capturing angles and five performing speeds for evaluation. The experimental result showed that the average accuracy was 89.17% in 30 frames per second.

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