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.

Quantify the difference between two arbitrary curves in space

R Package for Time Series Clustering Along with Optimizations for DTW

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

Digital signal analysis library for python. The library includes such methods of the signal analysis, signal processing and signal parameter estimation as ARMA-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mod…

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)

Dynamic Time Warping (DTW) library implementing lower bounds (LB_Keogh, LB_Improved...)

Comprehensive dynamic time warping module for python

Personal wake word detector

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

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".