audioLIME: Listenable Explanations Using Source Separation

Related tags

Deep LearningaudioLIME
Overview

audioLIME

This repository contains the Python package audioLIME, a tool for creating listenable explanations for machine learning models in music information retrival (MIR). audioLIME is based on the method lime (local interpretable model-agnostic explanations) work presented in this paper and uses source separation estimates in order to create interpretable components.

Citing

If you use audioLIME in your work, please cite it:

@misc{haunschmid2020audiolime,
    title={{audioLIME: Listenable Explanations Using Source Separation}},
    author={Verena Haunschmid and Ethan Manilow and Gerhard Widmer},
    year={2020},
    eprint={2008.00582},
    archivePrefix={arXiv},
    primaryClass={cs.SD},
    howpublished={13th International Workshop on Machine Learning and Music}
}

Publications

audioLIME is introduced/used in the following publications:

  • Verena Haunschmid, Ethan Manilow and Gerhard Widmer, audioLIME: Listenable Explanations Using Source Separation

  • Verena Haunschmid, Ethan Manilow and Gerhard Widmer, Towards Musically Meaningful Explanations Using Source Separation

Installation

The audioLIME package is not on PyPI yet. For installing it, clone the git repo and install it using setup.py.

git clone https://github.com/CPJKU/audioLIME.git  # HTTPS
git clone [email protected]:CPJKU/audioLIME.git  # SSH
cd audioLIME
python setup.py install

To install a version for development purposes check out this article.

Tests

To test your installation, the following test are available:

python -m unittest tests.test_SpleeterFactorization

python -m unittest tests.test_DataProviders

Note on Requirements

To keep it lightweight, not all possible dependencies are contained in setup.py. Depending on the factorization you want to use, you might need different packages, e.g. nussl or spleeter.

Installation & Usage of spleeter

pip install spleeter==2.0.2

When you're using spleeter for the first time, it will download the used model in a directory pretrained_models. You can only change the location by setting an environment variable MODEL_PATH before spleeter is imported. There are different ways to set an environment variable, for example:

export MODEL_PATH=/share/home/verena/experiments/spleeter/pretrained_models/

Available Factorizations

Currently we have the following factorizations implemented:

  • SpleeterFactorization based on the source separation system spleeter (code)
  • SoundLIMEFactorization: time-frequency segmentation based on SoundLIME (the original implementation was not flexible enough for our experiments)

Usage Example

Here we demonstrate how we can explain the prediction of FCN (code, Choi 2016, Won 2020) using SpleeterFactorization.

For this to work you need to install the requirements found in the above mentioned repo of the tagger and spleeter:

pip install -r examples/requirements.txt
    data_provider = RawAudioProvider(audio_path)
    spleeter_factorization = SpleeterFactorization(data_provider,
                                                   n_temporal_segments=10,
                                                   composition_fn=None,
                                                   model_name='spleeter:5stems')

    explainer = lime_audio.LimeAudioExplainer(verbose=True, absolute_feature_sort=False)

    explanation = explainer.explain_instance(factorization=spleeter_factorization,
                                             predict_fn=predict_fn,
                                             top_labels=1,
                                             num_samples=16384,
                                             batch_size=32
                                             )

For the details on setting everything up, see example_using_spleeter_fcn.

Listen to the input and explanation.

TODOs

  • upload to pypi.org (to allow installation via pip)
  • usage example for SoundLIMEFactorization
  • tutorial in form of a Jupyter Notebook
  • more documentation
You might also like...
Offical implementation for
Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021) by Qiming Hu, Xiaojie Guo. Dependencies P

Qiming Hu 31 Dec 20, 2022
PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集，包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

null 185 Dec 26, 2022
Source-to-Source Debuggable Derivatives in Pure Python
Source-to-Source Debuggable Derivatives in Pure Python

Tangent Tangent is a new, free, and open-source Python library for automatic differentiation. Existing libraries implement automatic differentiation b

Google 2.2k Jan 1, 2023
Empirical Study of Transformers for Source Code & A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

Transformers for variable misuse, function naming and code completion tasks The official PyTorch implementation of: Empirical Study of Transformers fo

Bayesian Methods Research Group 56 Nov 15, 2022
This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning"

CSP_Deep_EEG This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning" {https://www

Seyed Mahdi Roostaiyan 2 Nov 8, 2022
An open source machine learning library for performing regression tasks using RVM technique.

Introduction neonrvm is an open source machine learning library for performing regression tasks using RVM technique. It is written in C programming la

Siavash Eliasi 33 May 31, 2022
This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling.
This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling.

Locus This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order

Robotics and Autonomous Systems Group 96 Dec 15, 2022
This repository contains the source code for the paper
This repository contains the source code for the paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks",

DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks Project Page | Video | Presentation | Paper | Data L

Facebook Research 281 Dec 22, 2022
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

null 174 Dec 19, 2022
Releases(v0.0.3)
Owner
Institute of Computational Perception
Johannes Kepler University
Institute of Computational Perception
GitHub Repository
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
《Dual-Resolution Correspondence Network》(NeurIPS 2020)

Dual-Resolution Correspondence Network Dual-Resolution Correspondence Network, NeurIPS 2020 Dependency All dependencies are included in asset/dualrcne

Active Vision Laboratory 45 Nov 21, 2022
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation Where we are ? 12.27 目前和原论文仍有1%左右得差距，但已经力压很多SOTA了 ckpt__448_epoch_25.pth mIoU

zichengsaber 60 Dec 11, 2022
Fake-user-agent-traffic-geneator - Python CLI Tool to generate fake traffic against URLs with configurable user-agents

Fake traffic generator for Gartner Demo Generate fake traffic to URLs with custo

New Relic Experimental 3 Oct 31, 2022
PyTorch implementation of Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction (ICCV 2021).

Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction Introduction This is official PyTorch implementation of Towards Accurate Alignment

TANG Xiao 96 Dec 27, 2022
My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

Deep Q&A Table of Contents Presentation Installation Running Chatbot Web interface Results Pretrained model Improvements Upgrade Presentation This wor

Conchylicultor 2.9k Dec 28, 2022
a project for 3D multi-object tracking

a project for 3D multi-object tracking

155 Jan 04, 2023
The Python3 import playground

The Python3 import playground I have been confused about python modules and packages, this text tries to clear the topic up a bit. Sources: https://ch

Michael Moser 5 Feb 22, 2022
Roadmap to becoming a machine learning engineer in 2020

Roadmap to becoming a machine learning engineer in 2020, inspired by web-developer-roadmap.

Chris Hoyean Song 1.7k Dec 29, 2022
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning Authors: Yixuan Su, Fangyu Liu, Zaiqiao Meng, Lei Shu, Ehsan Shareghi, and Nig

Yixuan Su 79 Nov 04, 2022
This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

KGI (Knowledge Graph Induction) for slot filling This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code fo

International Business Machines 72 Jan 06, 2023
This repository is the official implementation of Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models Link to paper Abstract We study prediction of future out

Rickard Karlsson 2 Aug 19, 2022
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

yuanyao366 39 Dec 29, 2022
VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data

VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data Introduction Requirements Installation and Setup Supported Hardware and Software R

SigmaLab 1 Jun 14, 2022
The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

This repository is the official PyTorch implementation of SAINT. Find the paper on arxiv SAINT: Improved Neural Networks for Tabular Data via Row Atte

Gowthami Somepalli 284 Dec 21, 2022
Python framework for Stochastic Differential Equations modeling

SDElearn: a Python package for SDE modeling This package implements functionalities for working with Stochastic Differential Equations models (SDEs fo

4 May 10, 2022
Activating More Pixels in Image Super-Resolution Transformer

HAT [Paper Link] Activating More Pixels in Image Super-Resolution Transformer Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong BibTeX @article{ch

XyChen 270 Dec 27, 2022
Python script to download the celebA-HQ dataset from google drive

download-celebA-HQ Python script to download and create the celebA-HQ dataset. WARNING from the author. I believe this script is broken since a few mo

133 Dec 21, 2022
A toolkit for Lagrangian-based constrained optimization in Pytorch

Cooper About Cooper is a toolkit for Lagrangian-based constrained optimization in Pytorch. This library aims to encourage and facilitate the study of

Cooper 34 Jan 01, 2023
Official PyTorch implementation of PS-KD

Self-Knowledge Distillation with Progressive Refinement of Targets (PS-KD) Accepted at ICCV 2021, oral presentation Official PyTorch implementation of

61 Dec 28, 2022