The machine learning toolkit for time series analysis in Python

aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment)

DTW (Dynamic Time Warping) python module

Time series distances: Dynamic Time Warping (fast DTW implementation in C)

Python implementation of soft-DTW.

Transfer learning for time series classification

[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.

libfmp - Python package for teaching and learning Fundamentals of Music Processing (FMP)

Data augmentation using synthetic data for time series classification with deep residual networks

PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

An implementation of soft-DTW divergences.

Sync Toolbox - Python package with reference implementations for efficient, robust, and accurate music synchronization based on dynamic time warping (DTW)

Comprehensive dynamic time warping module for python

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…

A Python library for computing the Mel-Cepstral Distance (Mel-Cepstral Distortion, MCD) between two inputs. This implementation is based on the method proposed by Robert F. Kubichek in "Mel-Cepstral Distance Measure for Objective Speech Quality Assessment".

基于DTW与MFCC特征进行数字0-9的语音识别，DTW，MFCC，语音识别，中英数据，端点检测，Digital Voice Recognition。

wildboar is a Python module for temporal machine learning

Time Alignment Measurement for Time Series

10 digits recognition system based on DTW, HMM and GMM