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

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

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

Comprehensive dynamic time warping module for python

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

An implementation of soft-DTW divergences.

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…

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

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

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".

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

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。

10 digits recognition system based on DTW, HMM and GMM

Using MFCC feature and DTW algorithm to recognize rumber 0-9

Python implementation of the SparseDTW algorithm